# import libraries 

import requests # open web connection
import time
import datetime

# Connect to Website and pull in data
URL = 'https://www.50pros.com/fortune500'
# URL = 'http://localhost:9088'

# Set the headers for the Web Browser. 
# You can get it from 'https://httpbin.org/get' and take a look at 'User-Agent' element.
headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36", "Accept-Encoding":"gzip, deflate", "Accept":"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8", "DNT":"1","Connection":"close", "Upgrade-Insecure-Requests":"1"}

# Open web connection to the website
page = requests.get(URL, headers=headers)
print(page)

<Response [200]>

# Scrap the Web content for table
import pandas as pd
pd.set_option('display.max_rows', None)

df = pd.read_html(URL)
df[0]

# Save the scraped content into CSV file
# df.to_csv(r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\The_500_Largest_Companies_in_USA\Largest_500_Companies.csv', index = False)

##### Read 500 Largest Companies from stored CSV file

# Import all needed modules
import pandas as pd
import geopandas as gpd # pip install geopandas if it's not installed yet
import matplotlib.pyplot as plt
import mapclassify # pip install mapclassify if not yet installed
import folium

# Set to display all columns
pd.set_option('display.max_columns',None)
pd.set_option('display.max_rows',None)
pd.options.display.float_format = '{:,.0f}'.format
plt.rcParams['figure.figsize'] = (12, 8)

# Read the Dataframe from the CSV file
df = pd.read_csv(r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\The_500_Largest_Companies_in_USA\Largest_500_Companies.csv')

##### Data Cleaning
# Remove Duplicate data from raw table
df = df.drop_duplicates()

# Drop/remove all entries with blanks
# df.dropna(subset='temperature_2m', inplace=True)
# df.dropna(subset='relative_humidity_2m', inplace=True)
df.dropna(inplace=True)
df

# Check whether there's still any blank rows/columns
df[df.isna().any(axis=1)]

# Remove $ sign from Column 'Revenue (rounded)'
df['Revenue (rounded)'] = df['Revenue (rounded)'].str.replace('$','')
df['Revenue (rounded)'] = df['Revenue (rounded)'].str.replace(',','')

df['Employees'] = df['Employees'].str.replace(',','')

# Convert the type of Column 'Revenue (rounded)' into Float type
df['Revenue (rounded)'] = df['Revenue (rounded)'].astype(float)

# Convert the type of Column 'Employees' into Int type
df['Employees'] = df['Employees'].astype(int)

# Convert the type of Column 'Zip Code' into String type
df['Zip Code'] = df['Zip Code'].astype(str)
df['Zip Code'] = df['Zip Code'].str.zfill(5)

# Drop Column 'Rank' coz we don't need it
df.drop('Rank', axis=1, inplace=True)

df.head(2).style.format({'Employees': '{:,.0f}', 'Revenue (rounded)': '{:,.2f}'})

# Check whether there's still ZipCodes that less than 5 in length.
for index, row in df.iterrows():
    if len(row['Zip Code']) < 5:
        print("Ada yang panjangnya kurang dari 5")

# To mark that the For loop has finished.
print("Finished")

Finished

# Map of States in USA
gdfsts = gpd.read_file(r"C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\110m_cultural\ne_110m_admin_1_states_provinces.shp")
gdfsts = gdfsts.query("name != 'Hawaii'")
gdfsts = gdfsts.query("name != 'Alaska'")
gdfsts = gdfsts.rename(columns={'name': 'State'})
gdfsts = gdfsts[['State','region','postal','latitude','longitude','geometry']]

gdfsts.explore(column='State', cmap='hsv', edgecolor = 'black', legend=False)

# Draw the States Map of Fortune500 Companies
gdfsts500 = gdfsts.merge(df, how = 'inner', on = ['State'])
gdfsts500.explore(column='State', cmap='hsv', edgecolor='black', legend=False)

# Sneak peek at GeoDataframe of Fortune500 Companies
gdfsts500.head(2)

dfusact = pd.read_excel(r'C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\simplemaps_uszips_basicv1.91\uszips.xlsx', sheet_name='ZIP2')

# Convert the type of Column 'Zip Code' into String type
#dfusact['Zip Code'] = dfusact['Zip Code'].astype(int)
dfusact['Zip Code'] = dfusact['Zip Code'].astype(str)
dfusact['Zip Code'] = dfusact['Zip Code'].str.replace('.0','')
dfusact['Zip Code'] = dfusact['Zip Code'].str.zfill(5)
dfusact.head(2)

# Check Data Types of Geo location data

type(dfusact['Latitude'][0])

numpy.float64

type(dfusact['Longitude'][0])

numpy.float64

type(dfusact['Zip Code'][0])

str

# Create geometry column
geometry = gpd.points_from_xy(dfusact['Longitude'], dfusact['Latitude'], crs="EPSG:4326")

# Create GeoDataFrame
gdfusact = gpd.GeoDataFrame(dfusact, geometry=geometry)

# Check the data type of 'geometry' Column
type(gdfusact['geometry'])

geopandas.geoseries.GeoSeries

# Create Geodataframe of the Fortune500 cities with joining 'df' Dataframe with 'gdfusact' Dataframe
#gdfusact500 = gdfusact.merge(df, how = 'inner', on = ['City','State'])
gdfusact500 = gdfusact.merge(df, how = 'inner', on = ['Zip Code'])

# Let's explore one of the City in Fortune500 list
gdfusact500.where(gdfusact500['Zip Code'] == '44143').dropna().style.format({'Employees': '{:,.0f}', 'Revenue (rounded)': '{:,.2f}'})

# How Mayfield Village looks like
gdfusact500.query("City == 'Mayfield Village'").explore()

# US Fortune500 Companies Map (2025) color mapped by Population Column.
gdfusact500.explore(column='Population', name='Cities in Fortune500',cmap='RdYlGn')

# Conclusion: Fortune500 Companies are mostly located on Eastern half of mainland USA.

gdf2 = gpd.read_file(r"C:\PAULDATA\Paulus_Data_Analyst\Portfolio_Projects\nyc-zip-code-tabulation-areas-polygons.geojson")
gdf2

# Check how many Fortune500 Companies are located in New York City (turns out there are 43 Companies)
len(df.query("City == 'New York'"))

43

# Draw the map of New York City (complete) in detail (interesting due to many Fortune500 Companies are located here)
m = None
m = folium.Map(location = [40.6455, -74.0821], zoom_start=11)
tooltip = folium.features.GeoJsonTooltip(fields=['borough','postalCode'], aliases=["Borough","Postal Code"], labels=True, stick=False)
folium.GeoJson(gdf2, tooltip=tooltip).add_to(m)
folium.LayerControl().add_to(m)
m

# Using choropleth to give Color Map to the map.
m = None
m = folium.Map(location = [40.6455, -74.0821], zoom_start=11)
# The second Column for 'columns' (in this case: 'Shape_Leng') option must be numbers (int OR float)
cpleth = folium.Choropleth(gdf2, data=gdf2, key_on='feature.properties.postalCode', columns=['postalCode','Shape_Leng'], fill_color="RdYlGn", legend_name="Borough", name="New York City neighborhoods", line_opacity=1.0)
cpleth.geojson.add_child(tooltip)
cpleth.add_to(m)
folium.LayerControl().add_to(m)
m

# Grab all distinct State names
dfsts = df[['State']]
dfsts = dfsts.drop_duplicates()
dfsts = dfsts.reset_index(drop=True)
stslist = dfsts.to_numpy().tolist()
stslist[1][0]

for sts in stslist:
    print(sts[0])
    print("---------------")

Arkansas
---------------
Washington
---------------
Minnesota
---------------
California
---------------
Rhode Island
---------------
Nebraska
---------------
Texas
---------------
Pennsylvania
---------------
New York
---------------
Connecticut
---------------
Ohio
---------------
North Carolina
---------------
Michigan
---------------
Indiana
---------------
Missouri
---------------
Georgia
---------------
District of Columbia
---------------
Illinois
---------------
Virginia
---------------
Kentucky
---------------
New Jersey
---------------
Tennessee
---------------
Idaho
---------------
Maryland
---------------
Florida
---------------
Massachusetts
---------------
Oregon
---------------
Wisconsin
---------------
Colorado
---------------
Arizona
---------------
Oklahoma
---------------
Nevada
---------------
Iowa
---------------
Louisiana
---------------
Delaware
---------------
Alabama
---------------
Kansas
---------------

gdfsts

# Check the Largest Revenue Company in each State
dfmaxrevco = df.head(1)

for st in stslist[0:]:
    dfst = df.where(df['State'] == st[0]).dropna()
    mnmaxrev = dfst['Revenue (rounded)'].max()
    dfstmaxrevco = dfst.where(dfst['Revenue (rounded)'] == mnmaxrev).dropna()
    #dfstmaxrevco[['Rank']] = int(dfstmaxrevco['Rank'].iloc[0])
    dfstmaxrevco[['Zip Code']] = int(dfstmaxrevco['Zip Code'].iloc[0])
    #print(dfstmaxrevco)
    dfmaxrevco = pd.concat([dfmaxrevco, dfstmaxrevco], ignore_index=True)
    
dfmaxrevco = dfmaxrevco.drop(0)
dfmaxrevco[['State','Company','Industry','City','Zip Code','Revenue (rounded)']].sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

# Draw the Map of the Largest Revenue Companies in each State
# Join the Dataframe 'gdfsts500' with 'dfmaxrevco'
gdfstsmaxrevco = gdfsts.merge(dfmaxrevco, how = 'inner', on = ['State'])
# ,'Company','Industry','Zip Code','Website','Employees'
gdfstsmaxrevco.head(2)

m = gdfstsmaxrevco.explore(column='Revenue (rounded)', name='Biggest Revenue Company in each State', cmap='RdYlGn', edgecolor='black', legend_name="Revenue")
folium.LayerControl().add_to(m)
m

# Check the Smallest Revenue Company in each State
dfminrevco = df.head(1)

for st in stslist[0:]:
    dfst = df.where(df['State'] == st[0]).dropna()
    mnminrev = dfst['Revenue (rounded)'].min()
    dfstminrevco = dfst.where(dfst['Revenue (rounded)'] == mnminrev).dropna()
    dfstminrevco[['Zip Code']] = int(dfstminrevco['Zip Code'].iloc[0])
    dfminrevco = pd.concat([dfminrevco, dfstminrevco], ignore_index=True)
    
dfminrevco = dfminrevco.drop(0)
dfminrevco[['State','Company','Industry','City','Zip Code','Revenue (rounded)']].sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

# Draw the Map of the Smallest Revenue Company in each State
# Join the Dataframe 'gdfsts' with 'dfminrevco'
gdfstsminrevco = gdfsts.merge(dfminrevco, how = 'inner', on = ['State'])
gdfstsminrevco.head(2)

mminrevco = gdfstsminrevco.explore(column='Revenue (rounded)', name='Smallest Revenue Company in each State', cmap='RdYlGn', edgecolor='black')
folium.LayerControl().add_to(mminrevco)
mminrevco

# Check the Total Revenue in each State
dfsttr = df.groupby('State')[df.columns[[7]]].sum('Revenue (rounded)').sort_values('Revenue (rounded)', ascending=False)
dfsttr

# Draw the Map of Total Revenue in each State
# Join the Dataframe 'gdfsts' with 'dfsttr'
gdfststotrevco = gdfsts.merge(dfsttr, how = 'inner', on = ['State'])
gdfststotrevco.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

mtotrevco = gdfststotrevco.explore(column='Revenue (rounded)', name='Total Revenue of States', cmap='RdYlGn', edgecolor='black')
folium.LayerControl().add_to(mtotrevco)
mtotrevco

# Average revenue in each State
dfstavrrev = df.groupby('State')[df.columns[[7]]].mean().sort_values('Revenue (rounded)', ascending=False)
dfstavrrev

# Draw the Map of the Average Revenue of Companies in each State
# Join the Dataframe 'gdfsts' with 'dfstavr'
gdfstsstavrrev = gdfsts.merge(dfstavrrev, how = 'inner', on = ['State'])
gdfstsstavrrev.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True).head(2)

mstavrrev = gdfstsstavrrev.explore(column='Revenue (rounded)', name='Average Revenue of Companies in each State', cmap='RdYlGn', edgecolor='black')
folium.LayerControl().add_to(mstavrrev)
mstavrrev

# Largest Employer Company in each State
dfmaxempco = df.head(1)

for st in stslist[0:]:
    dfst = df.where(df['State'] == st[0]).dropna()
    mnmaxemp = dfst['Employees'].max()
    dfstmaxempco = dfst.where(dfst['Employees'] == mnmaxemp).dropna()
    dfmaxempco = pd.concat([dfmaxempco, dfstmaxempco], ignore_index=True)
    
dfmaxempco = dfmaxempco.drop(0)
#dfmaxempco['Employees'] = df['Employees'].astype(int)
dfmaxempco[['State','Company','Industry','City','Zip Code','Employees']].sort_values(by='Employees', ascending=False).reset_index(drop=True)

# The Biggest Employer Company in each State.
# Join the Dataframe 'gdfsts' with 'dfmaxempco'
gdfstsmaxempco = gdfsts.merge(dfmaxempco, how = 'inner', on = ['State'])
gdfstsmaxempco.head(2)

mmaxempco = gdfstsmaxempco.explore(column='Employees', name='Biggest Employer Company in each State', cmap='RdYlGn', edgecolor='black')
folium.LayerControl().add_to(mmaxempco)
mmaxempco

# Smallest Employer Company in each State
dfminempco = df.head(1)

for st in stslist[0:]:
    dfst = df.where(df['State'] == st[0]).dropna()
    mnminemp = dfst['Employees'].min()
    dfstminempco = dfst.where(dfst['Employees'] == mnminemp).dropna()
    dfminempco = pd.concat([dfminempco, dfstminempco], ignore_index=True)
    
dfminempco = dfminempco.drop(0)
dfminempco[['State','Company','Industry','City','Zip Code','Employees']].sort_values(by='Employees', ascending=False).reset_index(drop=True)

# The Smallest Employer Company in each State.
# Join the Dataframe 'gdfsts' with 'dfminempco'
gdfstsminempco = gdfsts.merge(dfminempco, how = 'inner', on = ['State'])
gdfstsminempco.head(2)

mminempco = gdfstsminempco.explore(column='Employees', name='Smallest Employer Company in each State', cmap='RdYlGn', edgecolor='black')
folium.LayerControl().add_to(mminempco)
mminempco

# Check Total Employee in each State
dfstte = df.groupby('State').sum('Employees').sort_values('Employees', ascending=False)
dfstte

# Map of Total Employee in each State.
# Join the Dataframe 'gdfsts' with 'dfstte'
gdfstsstte = gdfsts.merge(dfstte, how = 'inner', on = ['State'])
gdfstsstte.head(2)

mstte = gdfstsstte.explore(column='Employees', name='Total Employee in each State', cmap='RdYlGn', edgecolor='black')
folium.LayerControl().add_to(mstte)
mstte

# Check Average Number of Employee in each State
dfstavremp = df.groupby('State')[df.columns[[6]]].mean('Employees').sort_values('Employees', ascending=False)
dfstavremp

# The Average Number of Employee in each State.
# Join the Dataframe 'gdfsts' with 'dfstavremp'
gdfstsstavremp = gdfsts.merge(dfstavremp, how = 'inner', on = ['State'])
gdfstsstavremp.head(2)

mstavremp = gdfstsstavremp.explore(column='Employees', name='Average Number of Employee in each State', cmap='RdYlGn', edgecolor='black')
folium.LayerControl().add_to(mstavremp)
mstavremp

# Check the Most Efficient Company in each State
# Efficient here means how big Revenue is produced by each Employee.
dfstmaxeffco = df.head(1)
dfstmaxeffco.insert(0, 'Efficiency', 1) # Add new Column 'Efficiency' to new

for st in stslist[0:]:
    dfst = df.where(df['State'] == st[0]).dropna()
    dfst['Efficiency'] = dfst['Revenue (rounded)']/dfst['Employees']
    dfst[['Zip Code']] = int(dfst['Zip Code'].iloc[0])
    dfstmaxeffco = pd.concat([dfstmaxeffco, dfst.sort_values(by='Efficiency',ascending=False).head(1)], ignore_index=True)
    
dfstmaxeffco = dfstmaxeffco.drop(0)
dfstmaxeffco[['State','City','Company','Industry','Zip Code','Efficiency']].sort_values(by='Efficiency', ascending=False).reset_index(drop=True)

# The Most Efficient Company in each State.
# Join the Dataframe 'gdfsts' with 'dfstmaxeffco'
gdfstsmaxeffco = gdfsts.merge(dfstmaxeffco, how = 'inner', on = ['State'])
gdfstsmaxeffco.head(2)

mstmaxeffco = gdfstsmaxeffco.explore(column='Efficiency', name='Most Efficient Company in each State', cmap='RdYlGn', edgecolor='black')
folium.LayerControl().add_to(mstmaxeffco)
mstmaxeffco

# Check the Least Efficient Company in each State
dfstmineffco = df.head(1)
dfstmineffco.insert(0, 'Efficiency', 1) # Add new Column 'Efficiency' to new

for st in stslist[0:]:
    dfst = df.where(df['State'] == st[0]).dropna()
    dfst['Efficiency'] = dfst['Revenue (rounded)']/dfst['Employees']
    dfstmineffco = pd.concat([dfstmineffco, dfst.sort_values(by='Efficiency',ascending=True).head(1)], ignore_index=True)
    
dfstmineffco = dfstmineffco.drop(0)
dfstmineffco[['State','City','Company','Industry','Zip Code','Efficiency']].sort_values(by='Efficiency', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfsts' with 'dfstmineffco'
gdfstsmineffco = gdfsts.merge(dfstmineffco, how = 'inner', on = ['State']).sort_values('Efficiency', ascending=False).reset_index(drop=True)
gdfstsmineffco.head(2)

mstmineffco = gdfstsmineffco.explore(column='Efficiency', name='Least Efficient Company in each State', cmap='RdYlGn', edgecolor='black')
folium.LayerControl().add_to(mstmineffco)
mstmineffco

# Check the Average Efficiency in each State
dfstavreff = df.head(1)
dfstavreff.insert(0, 'Efficiency', 1) # Add new Column 'Efficiency' to new

for st in stslist[0:]:
    dfst = df.where(df['State'] == st[0]).dropna()
    dfst['Efficiency'] = dfst['Revenue (rounded)']/dfst['Employees']
    #print(dfct.groupby(['State','City'], as_index=False).mean('Efficiency'))
    dfstavreff = pd.concat([dfstavreff, dfst.groupby(['State'], as_index=False).mean('Efficiency')], ignore_index=True)[['State','Efficiency']]
    
dfstavreff = dfstavreff.drop(0)
dfstavreff.sort_values(by='Efficiency', ascending=False).reset_index(drop=True)

# Join the Dataframe 'dfstavreff' with 'df'
gdfstsstavreff = gdfsts.merge(dfstavreff, how = 'inner', on = ['State'])
gdfstsstavreff = gdfstavreff.sort_values('Efficiency', ascending=False).reset_index(drop=True)
gdfstsstavreff

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[58], line 3
      1 # Join the Dataframe 'dfstavreff' with 'df'
      2 gdfstsstavreff = gdfsts.merge(dfstavreff, how = 'inner', on = ['State'])
----> 3 gdfstsstavreff = gdfstavreff.sort_values('Efficiency', ascending=False).reset_index(drop=True)
      4 gdfstsstavreff

NameError: name 'gdfstavreff' is not defined

mstavreff = gdfstsstavreff.explore(column='Efficiency', name='The Average Efficiency in each State', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mstavreff)
mstavreff

# Grab all distinct City names
dfcts = df[['City','State']]
dfcts = dfcts.drop_duplicates()
dfcts = dfcts.sort_values('City', ascending=True).reset_index(drop=True)
ctslist = dfcts.to_numpy().tolist()
ctslist[1][0]

for cts in ctslist:
    print(cts[0], ',', cts[1])
    #print("---------------")

Abbott Park , Illinois
Akron , Ohio
Allentown , Pennsylvania
Ankeny , Iowa
Antioch , Tennessee
Arden Hills , Minnesota
Arlington , Virginia
Arlington , Texas
Armonk , New York
Ashburn , Virginia
Atlanta , Georgia
Auburn Hills , Michigan
Austin , Minnesota
Austin , Texas
Baltimore , Maryland
Beaverton , Oregon
Bellevue , Washington
Benton Harbor , Michigan
Bentonville , Arkansas
Bethesda , Maryland
Beverly Hills , California
Birmingham , Alabama
Bloomfield , Connecticut
Bloomfield Hills , Michigan
Bloomington , Illinois
Boise , Idaho
Bolingbrook , Illinois
Boston , Massachusetts
Brentwood , Tennessee
Buffalo , New York
Burbank , California
Burlington , Massachusetts
Burlington , New Jersey
Burlington , North Carolina
Byron Center , Michigan
Calhoun , Georgia
Cambridge , Massachusetts
Camden , New Jersey
Canonsburg , Pennsylvania
Centennial , Colorado
Chandler , Arizona
Charlotte , North Carolina
Chattanooga , Tennessee
Chesapeake , Virginia
Chesterfield , Missouri
Chicago , Illinois
Cincinnati , Ohio
Cleveland , Ohio
Columbus , Georgia
Columbus , Indiana
Columbus , Ohio
Conshohocken , Pennsylvania
Coral Gables , Florida
Coraopolis , Pennsylvania
Corning , New York
Corona , California
Cupertino , California
Dallas , Texas
Dearborn , Michigan
Deerfield , Illinois
Denver , Colorado
Des Moines , Iowa
Des Peres , Missouri
Detroit , Michigan
Downers Grove , Illinois
Dublin , Ohio
Dublin , California
Duluth , Georgia
Durham , North Carolina
Eden Prairie , Minnesota
El Dorado , Arkansas
El Segundo , California
Elkhart , Indiana
Englewood , Colorado
Erie , Pennsylvania
Estero , Florida
Evansville , Indiana
Evendale , Ohio
Fairfield , Ohio
Falls Church , Virginia
Farmington , Connecticut
Findlay , Ohio
Fort Lauderdale , Florida
Fort Washington , Pennsylvania
Fort Wayne , Indiana
Fort Worth , Texas
Foster City , California
Framingham , Massachusetts
Franklin , Tennessee
Franklin Lakes , New Jersey
Fremont , California
Glen Allen , Virginia
Glenview , Illinois
Goodlettsville , Tennessee
Greenwich , Connecticut
Hartford , Connecticut
Herndon , Virginia
Hershey , Pennsylvania
Houston , Texas
Indianapolis , Indiana
Inver Grove Heights , Minnesota
Irvine , California
Irving , Texas
Issaquah , Washington
Jackson , Michigan
Jacksonville , Florida
Johnston , Rhode Island
Juno Beach , Florida
King of Prussia , Pennsylvania
Kingsport , Tennessee
Lake Forest , Illinois
Lakeland , Florida
Lansing , Michigan
Las Vegas , Nevada
Livonia , Michigan
Long Beach , California
Long Island City , New York
Los Gatos , California
Louisville , Kentucky
Lowell , Arkansas
Madison , Wisconsin
Manhattan Beach , California
Maplewood , Minnesota
Marlborough , Massachusetts
Maumee , Ohio
Mayfield Village , Ohio
McLean , Virginia
Medford , Oregon
Melbourne , Florida
Melville , New York
Memphis , Tennessee
Menlo Park , California
Menomonee Falls , Wisconsin
Mentor , Ohio
Merriam , Kansas
Miami , Florida
Midland , Texas
Midland , Michigan
Milpitas , California
Milwaukee , Wisconsin
Minneapolis , Minnesota
Moline , Illinois
Monroe , Louisiana
Mooresville , North Carolina
Mountain View , California
Nashville , Tennessee
New Braunfels , Texas
New Britain , Connecticut
New Brunswick , New Jersey
New Orleans , Louisiana
New York , New York
Newark , California
Newark , New Jersey
Newport Beach , California
Newport News , Virginia
North Chicago , Illinois
Northbrook , Illinois
Norwalk , Connecticut
Oak Brook , Illinois
Oakland , California
Oklahoma City , Oklahoma
Omaha , Nebraska
Orlando , Florida
Orrville , Ohio
Oshkosh , Wisconsin
Palm Beach Gardens , Florida
Palo Alto , California
Parsippany , New Jersey
Philadelphia , Pennsylvania
Phoenix , Arizona
Pittsburgh , Pennsylvania
Plano , Texas
Plantation , Florida
Pleasanton , California
Portage , Michigan
Princeton , New Jersey
Providence , Rhode Island
Purchase , New York
Radnor , Pennsylvania
Rahway , New Jersey
Raleigh , North Carolina
Redmond , Washington
Redwood City , California
Reno , Nevada
Reston , Virginia
Richfield , Minnesota
Richmond , Virginia
Riverwoods , Illinois
Rolling Meadows , Illinois
Roseland , New Jersey
Rosemead , California
Rosemont , Illinois
Round Rock , Texas
San Antonio , Texas
San Diego , California
San Francisco , California
San Jose , California
San Mateo , California
Santa Clara , California
Scottsdale , Arizona
Seattle , Washington
Secaucus , New Jersey
Southfield , Michigan
Spring , Texas
Springdale , Arkansas
Springfield , Massachusetts
Springfield , Missouri
St. Louis , Missouri
St. Paul , Minnesota
St. Petersburg , Florida
Stamford , Connecticut
Summit , New Jersey
Sumner , Washington
Sunny Isles Beach , Florida
Sunnyvale , California
Tampa , Florida
Tarrytown , New York
Teaneck , New Jersey
Tempe , Arizona
Thousand Oaks , California
Toledo , Ohio
Tulsa , Oklahoma
Vernon Hills , Illinois
Victor , New York
Wallingford , Connecticut
Waltham , Massachusetts
Warsaw , Indiana
Washington , District of Columbia
Westchester , Illinois
Westerville , Ohio
Westlake , Texas
Westminster , Colorado
Wilmington , Delaware
Wilmington , Massachusetts
Winona , Minnesota
Woodland Hills , California
Woonsocket , Rhode Island

df.where(df['Zip Code'] == '44143').dropna()

# Check the Largest Revenue Company in each City
dfmaxrevco = df.head(1)

for ct in ctslist[0:]:
    dfct = df.where(df['City'] == ct[0]).where(df['State'] == ct[1]).dropna()
    mnmaxrev = dfct['Revenue (rounded)'].max()
    dfctmaxrevco = dfct.where(dfct['Revenue (rounded)'] == mnmaxrev).dropna()
    dfmaxrevco = pd.concat([dfmaxrevco, dfctmaxrevco], ignore_index=True)
    
dfmaxrevco = dfmaxrevco.drop(0)
dfmaxrevco[['City','State','Company','Industry','Zip Code','Revenue (rounded)']].sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfusact' with 'dfmaxrevco' for the Top 10
gdfusactmaxrevco = gdfusact.merge(dfmaxrevco, how = 'inner', on = ['Zip Code'])
gdfusactmaxrevco = gdfusactmaxrevco.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

mctmaxrevco = gdfusactmaxrevco.explore(column='Revenue (rounded)', name='Biggest Revenue Company in each City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mctmaxrevco)
mctmaxrevco

# Check the Smallest Revenue Company in each City
dfminrevco = df.head(1)

for ct in ctslist[0:]:
    dfct = df.where(df['City'] == ct[0]).where(df['State'] == ct[1]).dropna()
    mnminrev = dfct['Revenue (rounded)'].min()
    dfctminrevco = dfct.where(dfct['Revenue (rounded)'] == mnminrev).dropna()
    dfminrevco = pd.concat([dfminrevco, dfctminrevco], ignore_index=True)
    
dfminrevco = dfminrevco.drop(0)
dfminrevco[['City','State','Company','Industry','Zip Code','Revenue (rounded)']].sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

# Conclusion: fun fact about the Smallest Revenue Company in each City is Walmart is a Double Champion, both as the Biggest Revenue and the Smallest
# due to the fact that Walmart is the only Company in Bentonville goes into Fortune500 list.

# Join the Dataframe 'gdfusact' with 'dfminrevco' for the Top 10
gdfusactminrevco = gdfusact.merge(dfminrevco, how = 'inner', on = ['Zip Code'])
gdfusactminrevco = gdfusactminrevco.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

mctminrevco = gdfusactminrevco.explore(column='Revenue (rounded)', name='Smallest Revenue Company in each City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mctminrevco)
mctminrevco

# Check the Total Revenue in each City
dfcttr = df.groupby(['City','State'], as_index=False)[df.columns[[7]]].sum('Revenue (rounded)').sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)
dfcttr

# Join the Dataframe 'gdfusact' with 'dfcttr' for the Top 10
dfcttr = dfcttr.merge(df[['City','State','Company','Zip Code']], how = 'inner', on = ['City','State'])
gdfusactcttr = gdfusact.merge(dfcttr, how = 'inner', on = ['Zip Code'])
gdfusactcttr = gdfusactcttr.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

mcttr = gdfusactcttr.explore(column='Revenue (rounded)', name='Total Revenue in each City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mcttr)
mcttr

# Average revenue in each City
dfctavr = df.groupby(['City','State'], as_index=False)[df.columns[[7]]].mean('Revenue (rounded)').sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)
dfctavr

# Fun fact: again, Bentonville is the Champion due to the only one Company made it into Fortune500 list.

# Join the Dataframe 'gdfusact' with 'dfctavr' for the Top 10
dfctavr = dfctavr.merge(df[['City','State','Company','Zip Code']], how = 'inner', on = ['City','State'])
gdfusactctavr = gdfusact.merge(dfctavr, how = 'inner', on = ['Zip Code'])
gdfusactctavr = gdfusactctavr.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

mctctavr = gdfusactctavr.explore(column='Revenue (rounded)', name='Average Revenue in each City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mctctavr)
mctctavr

# Check the Largest Employer Company in each City
dfmaxempco = df.head(1)

for ct in ctslist[0:]:
    dfct = df.where(df['City'] == ct[0]).where(df['State'] == ct[1]).dropna()
    mnmaxemp = dfct['Employees'].max()
    dfctmaxempco = dfct.where(dfct['Employees'] == mnmaxemp).dropna()
    dfmaxempco = pd.concat([dfmaxempco, dfctmaxempco], ignore_index=True)
    
dfmaxempco = dfmaxempco.drop(0)
dfmaxempco[['City','State','Company','Industry','Zip Code','Employees']].sort_values(by='Employees', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfusact' with 'dfmaxempco' for the Top 10
gdfusactmaxempco = gdfusact.merge(dfmaxempco, how = 'inner', on = ['Zip Code'])
gdfusactmaxempco = gdfusactmaxempco.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

mctmaxempco = gdfusactmaxempco.explore(column='Revenue (rounded)', name='Biggest Employer Company in each City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mctmaxempco)
mctmaxempco

# Check the Smallest Employer Company in each City
dfminempco = df.head(1)

for ct in ctslist[0:]:
    dfct = df.where(df['City'] == ct[0]).where(df['State'] == ct[1]).dropna()
    mnminemp = dfct['Employees'].min()
    dfctminempco = dfct.where(dfct['Employees'] == mnminemp).dropna()
    dfminempco = pd.concat([dfminempco, dfctminempco], ignore_index=True)
    
dfminempco = dfminempco.drop(0)
dfminempco[['City','State','Company','Industry','Zip Code','Employees']].sort_values(by='Employees', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfusact' with 'dfminempco' for the Top 10
gdfusactminempco = gdfusact.merge(dfminempco, how = 'inner', on = ['Zip Code'])
gdfusactminempco = gdfusactminempco.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

mctminempco = gdfusactminempco.explore(column='Revenue (rounded)', name='Smallest Employer Company in each City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mctminempco)
mctminempco

# Check the Total Employee in each City
dfctte = df.groupby(['City','State'])[df.columns[[6]]].sum('Employees').sort_values('Employees', ascending=False)
dfctte

# Join the Dataframe 'gdfusact' with 'dfctte' for the Top 10
dfctte = dfctte.merge(df[['City','State','Company','Zip Code']], how = 'inner', on = ['City','State'])
gdfusactctte = gdfusact.merge(dfctte, how = 'inner', on = ['Zip Code'])
gdfusactctte = gdfusactctte.sort_values('Employees', ascending=False).reset_index(drop=True)
gdfusactctte

mctte = gdfusactctte.explore(column='Employees', name='Total Employees in each City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mctte)
mctte

# Check Average Number of Employee in each City
dfctavremp = df.groupby(['City','State'])[df.columns[[6]]].mean('Employees').sort_values('Employees', ascending=False)
dfctavremp

# Join the Dataframe 'gdfusact' with 'dfctavremp' for the Top 10
dfctavremp = dfctavremp.merge(df[['City','State','Company','Zip Code']], how = 'inner', on = ['City','State'])
gdfusactctavremp = gdfusact.merge(dfctavremp, how = 'inner', on = ['Zip Code'])
gdfusactctavremp = gdfusactctavremp.sort_values('Employees', ascending=False).reset_index(drop=True)

mctavremp = gdfusactctavremp.explore(column='Employees', name='Average Number of Employees in each City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mctavremp)
mctavremp

# Check the Most Efficient Company in each City
dfctmaxeffco = df.head(1)
dfctmaxeffco.insert(0, 'Efficiency', 1) # Add new Column 'Efficiency' to new

for ct in ctslist[0:]:
    dfct = df.where(df['City'] == ct[0]).where(df['State'] == ct[1]).dropna()
    dfct['Efficiency'] = dfct['Revenue (rounded)']/dfct['Employees']
    dfctmaxeffco = pd.concat([dfctmaxeffco, dfct.sort_values(by='Efficiency',ascending=False).head(1)], ignore_index=True)
    
dfctmaxeffco = dfctmaxeffco.drop(0)
dfctmaxeffco[['City','State','Company','Industry','Zip Code','Efficiency']].sort_values(by='Efficiency', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfusact' with 'dfctmaxeffco' for the Top 10
gdfusactmaxeffco = gdfusact.merge(dfctmaxeffco, how = 'inner', on = ['Zip Code'])
gdfusactmaxeffco = gdfusactmaxeffco.sort_values('Efficiency', ascending=False).reset_index(drop=True)

mctmaxeffco = gdfusactmaxeffco.explore(column='Efficiency', name='The Most Efficient Company in each City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mctmaxeffco)
mctmaxeffco

# Check the Least Efficient Company in each City
dfctmineffco = df.head(1)
dfctmineffco.insert(0, 'Efficiency', 1) # Add new Column 'Efficiency' to new

for ct in ctslist[0:]:
    dfct = df.where(df['City'] == ct[0]).where(df['State'] == ct[1]).dropna()
    dfct['Efficiency'] = dfct['Revenue (rounded)']/dfct['Employees']
    dfctmineffco = pd.concat([dfctmineffco, dfct.sort_values(by='Efficiency',ascending=True).head(1)], ignore_index=True)
    
dfctmineffco = dfctmineffco.drop(0)
dfctmineffco[['City','State','Company','Industry','Zip Code','Efficiency']].sort_values(by='Efficiency', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfusact' with 'dfctmineffco' for the Top 10
gdfusactmineffco = gdfusact.merge(dfctmineffco, how = 'inner', on = ['Zip Code'])
gdfusactmineffco = gdfusactmineffco.sort_values('Efficiency', ascending=False).reset_index(drop=True)

mctmineffco = gdfusactmineffco.explore(column='Efficiency', name='The Least Efficient Company in each City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mctmineffco)
mctmineffco

# Check the Average Efficiency in each City
dfctavreff = df.head(1)
dfctavreff.insert(0, 'Efficiency', 1) # Add new Column 'Efficiency' to new

for ct in ctslist[0:]:
    dfct = df.where(df['City'] == ct[0]).dropna().where(df['State'] == ct[1]).dropna()
    dfct['Efficiency'] = dfct['Revenue (rounded)']/dfct['Employees']
    #print(dfct.groupby(['State','City'], as_index=False).mean('Efficiency'))
    dfctavreff = pd.concat([dfctavreff, dfct.groupby(['State','City'], as_index=False).mean('Efficiency')], ignore_index=True)[['State','City','Efficiency']]
    
dfctavreff = dfctavreff.drop(0)
dfctavreff.sort_values(by='Efficiency', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfusact' with 'dfctavreff' for the Top 10
dfctavreff = dfctavreff.merge(df[['City','State','Company','Zip Code']], how = 'inner', on = ['City','State']).sort_values('Efficiency', ascending=False).reset_index(drop=True)
dfctavreff

gdfusactavreff = gdfusact.merge(dfctavreff, how = 'inner', on = ['Zip Code'])
gdfusactavreff = gdfusactavreff.sort_values('Efficiency', ascending=False).reset_index(drop=True)

mctavreff = gdfusactavreff.explore(column='Efficiency', name='The Average Efficiency in each City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mctavreff)
mctavreff

# Grab all distinct Industry names
dfins = df[['Industry']]
dfins = dfins.drop_duplicates()
dfins = dfins.reset_index(drop=True)
inslist = dfins.to_numpy().tolist()
inslist[1][0]

for ins in inslist:
    print(ins[0])
    print("---------------")

General Merchandisers
---------------
Internet Services and Retailing
---------------
Health Care: Insurance and Managed Care
---------------
Computers, Office Equipment
---------------
Health Care: Pharmacy and Other Services
---------------
Insurance: Property and Casualty (Stock)
---------------
Petroleum Refining
---------------
Wholesalers: Health Care
---------------
Commercial Banks
---------------
Computer Software
---------------
Motor Vehicles & Parts
---------------
Specialty Retailers: Other
---------------
Diversified Financials
---------------
Food & Drug Stores
---------------
Telecommunications
---------------
Semiconductors and Other Electronic Components
---------------
Insurance: Property and Casualty (Mutual)
---------------
Food Consumer Products
---------------
Entertainment
---------------
Mail, Package and Freight Delivery
---------------
Pharmaceuticals
---------------
Food Production
---------------
Household and Personal Products
---------------
Pipelines
---------------
Aerospace & Defense
---------------
Wholesalers: Food and Grocery
---------------
Insurance: Life, Health (Stock)
---------------
Health Care: Medical Facilities
---------------
Construction and Farm Machinery
---------------
Information Technology Services
---------------
Insurance: Life, Health (Mutual)
---------------
Airlines
---------------
Wholesalers: Electronics and Office Equipment
---------------
Mining, Crude-Oil Production
---------------
Specialty Retailers: Apparel
---------------
Network and Other Communications Equipment
---------------
Apparel
---------------
Beverages
---------------
Chemicals
---------------
Scientific, Photographic and Control Equipment
---------------
Energy
---------------
Medical Products and Equipment
---------------
Industrial Machinery
---------------
Tobacco
---------------
Homebuilders
---------------
Automotive Retailing, Services
---------------
Food Services
---------------
Financial Data Services
---------------
Real Estate
---------------
Metals
---------------
Utilities: Gas and Electric
---------------
Securities
---------------
Wholesalers: Diversified
---------------
Oil and Gas Equipment, Services
---------------
Hotels, Casinos, Resorts
---------------
Railroads
---------------
Engineering & Construction
---------------
Waste Management
---------------
Diversified Outsourcing Services
---------------
Packaging, Containers
---------------
Transportation and Logistics
---------------
Electronics, Electrical Equip.
---------------
Building Materials, Glass
---------------
Advertising, Marketing
---------------
Trucking, Truck Leasing
---------------
Home Equipment, Furnishings
---------------
Publishing, Printing
---------------

# Check the Largest Revenue Company in each Industry
dfinsmaxrevco = df.head(1)

for ins in inslist[0:]:
    dfin = df.where(df['Industry'] == ins[0]).dropna()
    mninmaxrev = dfin['Revenue (rounded)'].max()
    dfinmaxrevco = dfin.where(dfin['Revenue (rounded)'] == mninmaxrev).dropna()
    dfinsmaxrevco = pd.concat([dfinsmaxrevco, dfinmaxrevco], ignore_index=True)
    
dfinsmaxrevco = dfinsmaxrevco.drop(0)
dfinsmaxrevco[['Industry','State','City','Company','Zip Code','Revenue (rounded)']].sort_values(by='Revenue (rounded)', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfusact' with 'dfinsmaxrevco' for the Largest Revenue Company in each Industry visualized by City
gdfusactinsmaxrevco = gdfusact.merge(dfinsmaxrevco, how = 'inner', on = ['Zip Code'])
gdfusactinsmaxrevco = gdfusactinsmaxrevco.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfsts' with 'dfinsmaxrevco' for the Largest Revenue Company in each Industry visualized by State
gdfstsinsmaxrevco = gdfsts.merge(dfinsmaxrevco, how = 'inner', on = ['State'])
gdfstsinsmaxrevco = gdfstsinsmaxrevco.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

# Draw multi-layer Map visualizing The Largest Revenue Company in each Industry by City and State
minsmaxrc = gdfstsinsmaxrevco.explore(column='Revenue (rounded)', name='The Largest Revenue Company in each Industry visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactinsmaxrevco.explore(m=minsmaxrc, column='Revenue (rounded)', name='The Largest Revenue Company in each Industry visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(minsmaxrc)
minsmaxrc

# Check the Smallest Revenue Company in each Industry
dfinsminrevco = df.head(1)

for ins in inslist[0:]:
    dfin = df.where(df['Industry'] == ins[0]).dropna()
    mninsminrev = dfin['Revenue (rounded)'].min()
    dfinminrevco = dfin.where(dfin['Revenue (rounded)'] == mninsminrev).dropna()
    dfinsminrevco = pd.concat([dfinsminrevco, dfinminrevco], ignore_index=True)
    
dfinsminrevco = dfinsminrevco.drop(0)
dfinsminrevco[['Industry','State','City','Company','Zip Code','Revenue (rounded)']].sort_values(by='Revenue (rounded)', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfusact' with 'dfinsminrevco' for the Smallest Revenue Company in each Industry visualized by City
gdfusactinsminrevco = gdfusact.merge(dfinsminrevco, how = 'inner', on = ['Zip Code'])
gdfusactinsminrevco = gdfusactinsminrevco.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfsts' with 'dfinsminrevco' for the Smallest Revenue Company in each Industry visualized by State
gdfstsinsminrevco = gdfsts.merge(dfinsminrevco, how = 'inner', on = ['State'])
gdfstsinsminrevco = gdfstsinsminrevco.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

minsminrc = gdfstsinsminrevco.explore(column='Revenue (rounded)', name='The Smallest Revenue Company in each Industry visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactinsminrevco.explore(m=minsminrc, column='Revenue (rounded)', name='The Smallest Revenue Company in each Industry visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(minsminrc)
minsminrc

# Check the Biggest Revenue Industry in US (a.k.a Total Revenue in each Industry)
dfintr = df.groupby('Industry', as_index=False)[['Revenue (rounded)']].sum('Revenue (rounded)').sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)
dfintr

# Join the Dataframe 'gdfusact' with 'dfintr' for the Top 10
dfintr = dfintr.head(10).merge(df[['Industry','City','State','Company','Zip Code']], how = 'inner', on = ['Industry'])
gdfusactintr = gdfusact.merge(dfintr, how = 'inner', on = ['Zip Code'])
gdfusactintr = gdfusactintr.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfsts' with 'dfintr'
gdfstsintr = gdfsts.merge(dfintr, how = 'inner', on = ['State'])
gdfstsintr = gdfstsintr.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

# Check what Companies that are in Industry 'Internet Services and Retailing'.
dfintr1st = dfintr.where(dfintr['Industry'] == 'Internet Services and Retailing').dropna()
dfintr1st

# Draw the Map visualizing Total Revenue in each Industry by State and City
minstr = gdfstsintr.explore(column='Revenue (rounded)', name='The Total Revenue in each Industry visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactintr.explore(m=minstr, column='Revenue (rounded)', name='The Total Revenue in each Industry visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(minstr)
minstr

# Draw the Map visualizing the Companies that are in the Biggest Revenue Industry by State and City
gdfstsintr1st = gdfstsintr.where(gdfstsintr['Industry'] == 'Internet Services and Retailing').dropna()
gdfusactintr1st = gdfusactintr.where(gdfusactintr['Industry'] == 'Internet Services and Retailing').dropna()

minstr1st = gdfstsintr1st.explore(column='Revenue (rounded)', name='The Biggest Revenue Industry Companies visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactintr1st.explore(m=minstr1st, column='Revenue (rounded)', name='The Biggest Revenue Industry Companies visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(minstr1st)
minstr1st

# Check the Average Revenue in each Industry (a.k.a The Biggest Average Revenue Industry in US)
dfinavr = df.groupby('Industry')[df.columns[[7]]].mean('Revenue (rounded)').sort_values('Revenue (rounded)', ascending=False)
dfinavr

# Join the Dataframe 'gdfusact' with 'dfinavr' for the Top 10
dfinavrall = dfinavr.merge(df[['Industry','City','State','Company','Zip Code']], how = 'inner', on = ['Industry'])
dfinavr10 = dfinavr.head(10).merge(df[['Industry','City','State','Company','Zip Code']], how = 'inner', on = ['Industry'])
gdfusactinavr = gdfusact.merge(dfinavr10, how = 'inner', on = ['Zip Code'])
gdfusactinavr = gdfusactinavr.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfsts' with 'dfinavr'
gdfstsinavr = gdfsts.merge(dfinavr10, how = 'inner', on = ['State'])
gdfstsinavr = gdfstsinavr.sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

# Fun fact: turns out, there are other Companies that have exactly same ZipCode with McKesson in Irving, Texas.
# The impact on the map, McKesson company even doesn't show up.
dfinavrall.where(dfinavrall['Zip Code'] == '75039').dropna()

# Draw the Map visualizing Average Revenue in each Industry by State and City
minsavr = gdfstsinavr.explore(column='Revenue (rounded)', name='The Average Revenue in each Industry visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactinavr.explore(m=minsavr, column='Revenue (rounded)', name='The Average Revenue in each Industry visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(minsavr)
minsavr

# Draw the Map visualizing the Companies that are in the Biggest Average Revenue Industry by State and City
gdfstsinavr1st = gdfstsinavr.where(gdfstsinavr['Industry'] == 'Wholesalers: Health Care').dropna()
gdfusactinavr1st = gdfusactinavr.where(gdfusactinavr['Industry'] == 'Wholesalers: Health Care').dropna()

minsavr1st = gdfstsinavr1st.explore(column='Revenue (rounded)', name='The Biggest Average Revenue Industry Companies visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactinavr1st.explore(m=minsavr1st, column='Revenue (rounded)', name='The Biggest Average Revenue Industry Companies visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(minsavr1st)
minsavr1st

# Check the Biggest Employer Company in each Industry
dfinsmaxempco = df.head(1)

for ins in inslist[0:]:
    dfin = df.where(df['Industry'] == ins[0]).dropna()
    mninsmaxemp = dfin['Employees'].max()
    dfinmaxempco = dfin.where(dfin['Employees'] == mninsmaxemp).dropna()
    dfinsmaxempco = pd.concat([dfinsmaxempco, dfinmaxempco], ignore_index=True)
    
dfinsmaxempco = dfinsmaxempco.drop(0)
dfinsmaxempco = dfinsmaxempco[['Industry','State','City','Company','Zip Code','Employees']].sort_values(by='Employees', ascending=False).reset_index(drop=True)
dfinsmaxempco

# Join the Dataframe 'gdfusact' with 'dfinsmaxempco' for the Biggest Employer Company in each Industry visualized by City
dfinsmaxempco = dfinsmaxempco.head(10)
gdfusactinsmaxempco = gdfusact.merge(dfinsmaxempco, how = 'inner', on = ['Zip Code'])
gdfusactinsmaxempco = gdfusactinsmaxempco.sort_values('Employees', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfsts' with 'dfinsmaxempco' for the Biggest Employer Company in each Industry visualized by State
gdfstsinmaxempco = gdfsts.merge(dfinsmaxempco, how = 'inner', on = ['State'])
gdfstsinmaxempco = gdfstsinmaxempco.sort_values('Employees', ascending=False).reset_index(drop=True)

# Draw the Map visualizing the Biggest Employer Company in each Industry by State and City
minsmaxempco = gdfstsinmaxempco.explore(column='Employees', name='The Biggest Employer in each Industry visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactinsmaxempco.explore(m=minsmaxempco, column='Employees', name='The Biggest Employer in each Industry visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(minsmaxempco)
minsmaxempco

# Check the Smallest Employer Company in each Industry
dfinsminempco = df.head(1)

for ins in inslist[0:]:
    dfin = df.where(df['Industry'] == ins[0]).dropna()
    mninsminemp = dfin['Employees'].min()
    dfinminempco = dfin.where(dfin['Employees'] == mninsminemp).dropna()
    dfinsminempco = pd.concat([dfinsminempco, dfinminempco], ignore_index=True)
    
dfinsminempco = dfinsminempco.drop(0)
dfinsminempco = dfinsminempco[['Industry','State','City','Company','Zip Code','Employees']].sort_values(by='Employees', ascending=False).reset_index(drop=True)
dfinsminempco

# Join the Dataframe 'gdfusact' with 'dfinsminempco' for the Smallest Employer Company in each Industry visualized by City
dfinsminempco = dfinsminempco.head(10)
gdfusactinsminempco = gdfusact.merge(dfinsminempco, how = 'inner', on = ['Zip Code'])
gdfusactinsminempco = gdfusactinsminempco.sort_values('Employees', ascending=False).reset_index(drop=True)

# Join the Dataframe 'gdfsts' with 'dfinsminempco' for the Smallest Employer Company in each Industry visualized by State
gdfstsinminempco = gdfsts.merge(dfinsminempco, how = 'inner', on = ['State'])
gdfstsinminempco = gdfstsinminempco.sort_values('Employees', ascending=False).reset_index(drop=True)

minsminempco = gdfstsinminempco.explore(column='Employees', name='The Smallest Employer Company in each Industry visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactinsminempco.explore(m=minsminempco, column='Employees', name='The Smallest Employer Company in each Industry visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(minsminempco)
minsminempco

# Check the Biggest Revenue Industry in each State.
# We use PARTITION BY-like feature to grouping but not making any new Index.
dummy = {'Revenue (rounded)': [1],
           'State': ['Dummy'],
            'Industry': ['Dummy']}
dfstmaxrevin = pd.DataFrame(dummy)
dfst = None

for st in stslist[0:]:
    dfst = df.where(df['State'] == st[0]).dropna()
    #print(dfst)
    dfstmaxrevin = pd.concat([dfstmaxrevin, dfst.groupby(['State','Industry'], as_index=False).sum("Revenue (rounded)").sort_values(by=['Revenue (rounded)'], ascending=False)[['State','Industry','Revenue (rounded)']].head(1)])
    
dfstmaxrevin = dfstmaxrevin.iloc[1:]
dfstmaxrevin = dfstmaxrevin[['State','Industry','Revenue (rounded)']].sort_values(by=['Revenue (rounded)'], ascending=False).reset_index(drop=True)
dfstmaxrevin

# Conclusion: the Biggest Revenue Industry is Petroleum Refining

dfstmaxrevin = dfstmaxrevin.head(10)
dfstmaxrevin = dfstmaxrevin.merge(df[['State','Company','Industry','Zip Code']], how = 'inner', on = ['State','Industry'])
dfstmaxrevin = dfstmaxrevin[['State','Industry','Company','Zip Code','Revenue (rounded)']].sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)
dfstmaxrevin

gdfstsmaxrevin = gdfsts.merge(dfstmaxrevin, how = 'inner', on = ['State'])
gdfusactmaxrevin = gdfusact.merge(dfstmaxrevin, how = 'inner', on = ['Zip Code'])
gdfusactmaxrevin
mstmaxrevin = gdfstsmaxrevin.explore(column='Revenue (rounded)', name='The Biggest Revenue Industry in each State visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactmaxrevin.explore(m=mstmaxrevin, column='Revenue (rounded)', name='The Biggest Revenue Company in each State visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mstmaxrevin)
mstmaxrevin

# Check the Smallest Revenue Industry in each State.
# We use PARTITION BY-like feature to grouping but not making any new Index.
dummy = {'Revenue (rounded)': [1],
           'State': ['Dummy'],
            'Industry': ['Dummy']}
dfstminrevin = pd.DataFrame(dummy)
dfst = None

for st in stslist[0:]:
    dfst = df.where(df['State'] == st[0]).dropna()
    dfstminrevin = pd.concat([dfstminrevin, dfst.groupby(['State','Industry'], as_index=False).sum("Revenue (rounded)").sort_values(by=['Revenue (rounded)'], ascending=True)[['State','Industry','Revenue (rounded)']].head(1)])
dfstminrevin = dfstminrevin.iloc[1:]
dfstminrevin[['State','Industry','Revenue (rounded)']].sort_values(by=['Revenue (rounded)'], ascending=False).reset_index(drop=True)

dfstminrevin = dfstminrevin.merge(df[['State','Company','Industry','Zip Code']], how = 'inner', on = ['State','Industry'])
dfstminrevin = dfstminrevin[['State','Industry','Company','Zip Code','Revenue (rounded)']].sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)
dfstminrevin

gdfstsminrevin = gdfsts.merge(dfstminrevin, how = 'inner', on = ['State'])
gdfusactminrevin = gdfusact.merge(dfstminrevin, how = 'inner', on = ['Zip Code']).sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)
gdfusactminrevin

mstminrevin = gdfstsminrevin.explore(column='Revenue (rounded)', name='The Smallest Revenue Industry in each State visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactminrevin.explore(m=mstminrevin, column='Revenue (rounded)', name='The Smallest Revenue Company in each State visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mstminrevin)
mstminrevin

# Check the Biggest Employer Industry in each State.
# We use PARTITION BY-like feature to grouping but not making any new Index.
dummy = {'Employees': [1],
           'State': ['Dummy'],
            'Industry': ['Dummy']}
dfstmaxempin = pd.DataFrame(dummy)

for st in stslist[0:]:
    dfst = df.where(df['State'] == st[0]).dropna()
    dfstmaxempin = pd.concat([dfstmaxempin, dfst.groupby(['State','Industry'], as_index=False).sum('Employees').sort_values(by=['Employees'], 
                        ascending=False)[['State','Industry','Employees']].head(1)])
    
dfstmaxempin = dfstmaxempin.iloc[1:]
dfstmaxempin[['State','Industry','Employees']].sort_values(by=['Employees'], ascending=False).reset_index(drop=True)

# Conclusion: the Biggest Employer Industry is General Merchandisers.

dfstmaxempin = dfstmaxempin.merge(df[['State','Company','Industry','Zip Code']], how = 'inner', on = ['State','Industry'])
dfstmaxempin[['State','Industry','Company','Zip Code','Employees']].sort_values('Employees', ascending=False).reset_index(drop=True)

gdfstsmaxempin = gdfsts.merge(dfstmaxempin, how = 'inner', on = ['State'])
gdfusactmaxempin = gdfusact.merge(dfstmaxempin, how = 'inner', on = ['Zip Code']).sort_values('Employees', ascending=False).reset_index(drop=True)
gdfusactmaxempin

mstmaxempin = gdfstsmaxempin.explore(column='Employees', name='The Biggest Employer Industry in each State visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactmaxempin.explore(m=mstmaxempin, column='Employees', name='The Biggest Employer Industry in each State visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mstmaxempin)
mstmaxempin

# Check the Smallest Employer Industry in each State.
# We use PARTITION BY-like feature to grouping but not making any new Index.
dummy = {'Employees': [1],
           'State': ['Dummy'],
            'Industry': ['Dummy']}
dfstminempin = pd.DataFrame(dummy)

for st in stslist[0:]:
    dfst = df.where(df['State'] == st[0]).dropna()
    dfstminempin = pd.concat([dfstminempin, dfst.groupby(['State','Industry'], as_index=False).sum('Employees').sort_values(by=['Employees'], 
                        ascending=True)[['State','Industry','Employees']].head(1)])

    
dfstminempin = dfstminempin.iloc[1:]
dfstminempin = dfstminempin.reset_index(drop=True)
dfstminempin[['State','Industry','Employees']].sort_values(by=['Employees'], ascending=False).reset_index(drop=True)

dfstminempin = dfstminempin.merge(df[['State','Company','Industry','Zip Code']], how = 'inner', on = ['State','Industry'])
dfstminempin[['State','Industry','Company','Zip Code','Employees']].sort_values('Employees', ascending=False).reset_index(drop=True)

gdfstsminempin = gdfsts.merge(dfstminempin, how = 'inner', on = ['State'])
gdfusactminempin = gdfusact.merge(dfstminempin, how = 'inner', on = ['Zip Code']).sort_values('Employees', ascending=False).reset_index(drop=True)
gdfusactminempin

mstminempin = gdfstsminempin.explore(column='Employees', name='The Smallest Employer Industry in each State visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactminempin.explore(m=mstminempin, column='Employees', name='The Smallest Employer Industry in each State visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mstminempin)
mstminempin

# Check the Biggest Revenue Industry in US (all States combined) (other viewpoint)
# a.k.a the Biggest Total Revenue Industry in US.
dfusmaxrevin = df.groupby(['Industry'], as_index=False).sum('Revenue (rounded)').sort_values(by=['Revenue (rounded)'], ascending=False)[['Industry','Revenue (rounded)']].reset_index(drop=True)
dfusmaxrevin10 = dfusmaxrevin.head(10)
dfusmaxrevin

# Conclusion: the Biggest Revenue Industry in US is 'Internet Services and Retailing'.

dfusmaxrevin10 = dfusmaxrevin10.merge(df[['State','Company','Industry','Zip Code']], how = 'inner', on = ['Industry'])
dfusmaxrevin10[['Industry','Company','State','Zip Code','Revenue (rounded)']].sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)

gdfstsusmaxrevin10 = gdfsts.merge(dfusmaxrevin10, how = 'inner', on = ['State'])
gdfusactusmaxrevin10 = gdfusact.merge(dfusmaxrevin10, how = 'inner', on = ['Zip Code']).sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)
gdfusactusmaxrevin10

musmaxrevin = gdfstsusmaxrevin10.explore(column='Revenue (rounded)', name='The Biggest Revenue Industry in US visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactusmaxrevin10.explore(m=musmaxrevin, column='Revenue (rounded)', name='The Biggest Revenue Industry in US visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(musmaxrevin)
musmaxrevin

# Check the Biggest Employer Industry in US (all States combined)
dfusmaxempin = df.groupby(['Industry'], as_index=False).sum('Employees').sort_values(by=['Employees'], ascending=False)[['Industry','Employees']].reset_index(drop=True)
dfusmaxempin10 = dfusmaxempin.head(10)
dfusmaxempin

# Conclusion: the Biggest Employer Industry in US is General Merchandisers.

dfusmaxempin10 = dfusmaxempin10.merge(df[['State','Company','Industry','Zip Code']], how = 'inner', on = ['Industry'])
dfusmaxempin10[['Industry','Company','State','Zip Code','Employees']].sort_values('Employees', ascending=False).reset_index(drop=True)

gdfstsusmaxempin10 = gdfsts.merge(dfusmaxempin10, how = 'inner', on = ['State'])
gdfusactusmaxempin10 = gdfusact.merge(dfusmaxempin10, how = 'inner', on = ['Zip Code']).sort_values('Employees', ascending=False).reset_index(drop=True)
gdfusactusmaxempin10

musmaxempin10 = gdfstsusmaxempin10.explore(column='Employees', name='The Biggest Employer Industry in US visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactusmaxempin10.explore(m=musmaxempin10, column='Employees', name='The Biggest Employer Industry in US visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(musmaxempin10)
musmaxempin10

# Check the Most Efficient Company in each Industry
dfinsmaxeffco = df.head(1)
dfinsmaxeffco.insert(0, 'Efficiency', 1) # Add new Column 'Efficiency' to new 

for ins in inslist[0:]:
    dfin = df.where(df['Industry'] == ins[0]).dropna()
    dfin['Efficiency'] = dfin['Revenue (rounded)']/dfin['Employees']
    dfinsmaxeffco = pd.concat([dfinsmaxeffco, dfin.sort_values(by='Efficiency',ascending=False).head(1)], ignore_index=True)
    
dfinsmaxeffco = dfinsmaxeffco.drop(0)
dfinsmaxeffco[['Industry','State','City','Company','Zip Code','Efficiency']].sort_values(by='Efficiency', ascending=False).reset_index(drop=True)

gdfstsinsmaxeffco = gdfsts.merge(dfinsmaxeffco, how = 'inner', on = ['State'])
gdfusactinsmaxeffco = gdfusact.merge(dfinsmaxeffco, how = 'inner', on = ['Zip Code']).sort_values('Efficiency', ascending=False).reset_index(drop=True)
gdfusactinsmaxeffco

minsmaxeffco = gdfstsinsmaxeffco.explore(column='Efficiency', name='The Most Efficient Company in each Industry visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactinsmaxeffco.explore(m=minsmaxeffco, column='Efficiency', name='The Most Efficient Company in each Industry visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(minsmaxeffco)
minsmaxeffco

# Check the Most Efficient Industry in each State.
# We use PARTITION BY-like feature to grouping but not making any new Index.
pd.set_option('display.max_rows',None)
dfstmaxeffin = df.iloc[0:1]

for st in stslist[0:]:
    dftemp = df.where(df['State'] == st[0]).dropna()
    dftemp.insert(6,'Efficiency',1)
    dftemp['Efficiency'] = dftemp['Revenue (rounded)'] / dftemp['Employees']
    dfstmaxeffin = pd.concat([dfstmaxeffin, dftemp.groupby(['State','Industry'], as_index=False).sum('Efficiency').sort_values('Efficiency', ascending=False).head(1)])

dfstmaxeffin = dfstmaxeffin.iloc[1:]
dfstmaxeffin = dfstmaxeffin[['State','Industry','Efficiency']].sort_values(by=['Efficiency'], ascending=False).reset_index(drop=True)
dfstmaxeffin10 = dfstmaxeffin.head(10)
dfstmaxeffin

dfstmaxeffin10 = dfstmaxeffin10.merge(df[['State','Company','Industry','Zip Code']], how = 'inner', on = ['State','Industry'])
dfstmaxeffin10[['State','Industry','Company','Zip Code','Efficiency']].sort_values('Efficiency', ascending=False).reset_index(drop=True)

gdfstsstmaxeffin10 = gdfsts.merge(dfstmaxeffin10, how = 'inner', on = ['State'])
gdfusactstmaxeffin10 = gdfusact.merge(dfstmaxeffin10, how = 'inner', on = ['Zip Code']).sort_values('Efficiency', ascending=False).reset_index(drop=True)
gdfusactstmaxeffin10

mstmaxeffin = gdfstsstmaxeffin10.explore(column='Efficiency', name='The Most Efficient Industry in each State visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactstmaxeffin10.explore(m=mstmaxeffin, column='Efficiency', name='The Most Efficient Industry in each State visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mstmaxeffin)
mstmaxeffin

# Conclusion:
# 1. Even though California is known with its Silicon Valley and movie Industry, but the Most Efficient one is not one of them. It's Wholesalers: Diversified.
# 2. Even though New York state has many companies in Commercial Banks Industry, turns out the Most Efficient one is Diversified Financials.
# 3. Many energy-related Companies are identified as the Most Efficient Industry.

# Check the Least Efficient Industry in each State.
# We use PARTITION BY-like feature to grouping but not making any new Index.
pd.set_option('display.max_rows',None)
dfstmineffin = df.iloc[0:1]

for st in stslist[0:]:
    dftemp = df.where(df['State'] == st[0]).dropna()
    dftemp.insert(6,'Efficiency',1)
    dftemp['Efficiency'] = dftemp['Revenue (rounded)'] / dftemp['Employees']
    dfstmineffin = pd.concat([dfstmineffin, dftemp.groupby(['State','Industry'], as_index=False).sum('Efficiency').sort_values('Efficiency', ascending=True).head(1)])

dfstmineffin = dfstmineffin.iloc[1:]
dfstmineffin = dfstmineffin[['State','Industry','Efficiency']].sort_values(by=['Efficiency'], ascending=False).reset_index(drop=True)
dfstmineffin10 = dfstmineffin.head(10)
dfstmineffin

# Conclusion:
# 1. Walmart as the Biggest Revenue Company and the Biggest Employer Company in State category, turns out, in terms of Efficiency, 
#    becomes one of the least.
# 2. Information Technology Services is the Lowest Efficient Industry. It's intriguing due to selling services is not the same as selling hardwares or softwares.
#    It just needs skills, knowledges, and experiences. 😕

dfstmineffin10 = dfstmineffin10.merge(df[['State','Company','Industry','Zip Code']], how = 'inner', on = ['State','Industry'])
dfstmineffin10[['State','Industry','Company','Zip Code','Efficiency']].sort_values('Efficiency', ascending=False).reset_index(drop=True)

gdfstsstmineffin10 = gdfsts.merge(dfstmineffin10, how = 'inner', on = ['State'])
gdfusactstmineffin10 = gdfusact.merge(dfstmineffin10, how = 'inner', on = ['Zip Code']).sort_values('Efficiency', ascending=False).reset_index(drop=True)
gdfusactstmineffin10

mstmineffin10 = gdfstsstmineffin10.explore(column='Efficiency', name='The Least Efficient Industry in each State visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactstmineffin10.explore(m=mstmineffin10, column='Efficiency', name='The Least Efficient Industry in each State visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(mstmineffin10)
mstmineffin10

# The Most Efficient Industry in US
dfusmaxeffin = df.groupby(['Industry'], as_index=False).sum('Employees')
dfusmaxeffin.insert(3,'Efficiency',1)
dfusmaxeffin['Efficiency'] = dfusmaxeffin['Revenue (rounded)'] / dfusmaxeffin['Employees']
dfusmaxeffin = dfusmaxeffin.sort_values('Efficiency', ascending=False).reset_index(drop=True)[['Industry','Employees','Revenue (rounded)','Efficiency']]
dfusmaxeffin

# Conclusion: Energy-related, Health Care, Insurance, and Construction are the Most Efficient Industries in US.

# Draw the 10 Most Efficient Industries in US
dfusmaxeffin10 = dfusmaxeffin.head(10)

dfusmaxeffin10 = dfusmaxeffin10.merge(df[['State','Company','Industry','Zip Code']], how = 'inner', on = ['Industry'])
dfusmaxeffin10[['State','Industry','Company','Zip Code','Efficiency']].sort_values('Efficiency', ascending=False).reset_index(drop=True)

gdfstsusmaxeffin10 = gdfsts.merge(dfusmaxeffin10, how = 'inner', on = ['State'])
gdfusactusmaxeffin10 = gdfusact.merge(dfusmaxeffin10, how = 'inner', on = ['Zip Code']).sort_values('Efficiency', ascending=False).reset_index(drop=True)
gdfusactusmaxeffin10

musmaxeffin10 = gdfstsusmaxeffin10.explore(column='Efficiency', name='The Most Efficient Industry in US visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactusmaxeffin10.explore(m=musmaxeffin10, column='Efficiency', name='The Most Efficient Industry in US visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(musmaxeffin10)
musmaxeffin10

# The Biggest Revenue City in US (other viewpoint)
dfusmaxrevct = df.groupby(['State','City'], as_index=False).sum('Revenue (rounded)').sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)
dfusmaxrevct10 = dfusmaxrevct.head(10)
dfusmaxrevct

dfusmaxrevct10 = dfusmaxrevct10.merge(df[['State','City','Company','Zip Code']], how = 'inner', on = ['State','City'])

gdfstsusmaxrevct10 = gdfsts.merge(dfusmaxrevct10, how = 'inner', on = ['State'])
gdfusactusmaxrevct10 = gdfusact.merge(dfusmaxrevct10, how = 'inner', on = ['Zip Code']).sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)
gdfusactusmaxrevct10

musmaxrevct10 = gdfstsusmaxrevct10.explore(column='Revenue (rounded)', name='The Biggest Revenue City in US visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactusmaxrevct10.explore(m=musmaxrevct10, column='Revenue (rounded)', name='The Biggest Revenue City in US visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(musmaxrevct10)
musmaxrevct10

# The Biggest Revenue State in US (other viewpoint)
dfusmaxrevst = df.groupby(['State'], as_index=False).sum('Revenue (rounded)').sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)
dfusmaxrevst10 = dfusmaxrevst.head(10)
dfusmaxrevst

dfusmaxrevst10 = dfusmaxrevst10.merge(df[['State','City','Company','Zip Code']], how = 'inner', on = ['State'])

gdfstsusmaxrevst10 = gdfsts.merge(dfusmaxrevst10, how = 'inner', on = ['State'])
gdfusactusmaxrevst10 = gdfusact.merge(dfusmaxrevst10, how = 'inner', on = ['Zip Code']).sort_values('Revenue (rounded)', ascending=False).reset_index(drop=True)
gdfusactusmaxrevst10

musmaxrevst10 = gdfstsusmaxrevst10.explore(column='Revenue (rounded)', name='The Biggest Revenue State in US visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactusmaxrevst10.explore(m=musmaxrevst10, column='Revenue (rounded)', name='The Biggest Revenue State in US visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(musmaxrevst10)
musmaxrevst10

# The Biggest Employer City in US (other viewpoint)
dfusmaxempct = df.groupby(['State','City'], as_index=False).sum('Employees').sort_values('Employees', ascending=False).reset_index(drop=True)
dfusmaxempct10 = dfusmaxempct.head(10)
dfusmaxempct

dfusmaxempct10 = dfusmaxempct10.merge(df[['State','City','Company','Zip Code']], how = 'inner', on = ['State','City'])

gdfstsusmaxempct10 = gdfsts.merge(dfusmaxempct10, how = 'inner', on = ['State'])
gdfusactusmaxempct10 = gdfusact.merge(dfusmaxempct10, how = 'inner', on = ['Zip Code']).sort_values('Employees', ascending=False).reset_index(drop=True)
gdfusactusmaxempct10
gdfstsusmaxempct10

musmaxempct10 = gdfstsusmaxempct10.explore(column='Employees', name='The Biggest Employer City in US visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactusmaxempct10.explore(m=musmaxempct10, column='Employees', name='The Biggest Employer City in US visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(musmaxempct10)
musmaxempct10

# The Biggest Employer State in US (other viewpoint)
dfusmaxempst = df.groupby(['State'], as_index=False).sum('Employees').sort_values('Employees', ascending=False).reset_index(drop=True)
dfusmaxempst10 = dfusmaxempst.head(10)
dfusmaxempst

dfusmaxempst10 = dfusmaxempst10.merge(df[['State','City','Company','Zip Code']], how = 'inner', on = ['State'])

gdfstsusmaxempst10 = gdfsts.merge(dfusmaxempst10, how = 'inner', on = ['State'])
gdfusactusmaxempst10 = gdfusact.merge(dfusmaxempst10, how = 'inner', on = ['Zip Code']).sort_values('Employees', ascending=False).reset_index(drop=True)
gdfusactusmaxempst10

musmaxempst10 = gdfstsusmaxempst10.explore(column='Employees', name='The Biggest Employer State in US visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactusmaxempst10.explore(m=musmaxempst10, column='Employees', name='The Biggest Employer State in US visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(musmaxempst10)
musmaxempst10

# The Most Efficient City in US
dfusmaxeffct = df.groupby(['State','City'], as_index=False).sum('Employees')
dfusmaxeffct.insert(4,'Efficiency',1)
dfusmaxeffct['Efficiency'] = dfusmaxeffct['Revenue (rounded)'] / dfusmaxeffct['Employees']
dfusmaxeffct = dfusmaxeffct.sort_values('Efficiency', ascending=False).reset_index(drop=True)
dfusmaxeffct10 = dfusmaxeffct.head(10)
dfusmaxeffct

dfusmaxeffct10 = dfusmaxeffct10.merge(df[['State','City','Company','Zip Code']], how = 'inner', on = ['State','City'])

gdfstsusmaxeffct10 = gdfsts.merge(dfusmaxeffct10, how = 'inner', on = ['State'])
gdfusactusmaxeffct10 = gdfusact.merge(dfusmaxeffct10, how = 'inner', on = ['Zip Code']).sort_values('Efficiency', ascending=False).reset_index(drop=True)
gdfusactusmaxeffct10

musmaxeffct10 = gdfstsusmaxeffct10.explore(column='Efficiency', name='The Most Efficient City in US visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactusmaxeffct10.explore(m=musmaxeffct10, column='Efficiency', name='The Most Efficient City in US visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(musmaxeffct10)
musmaxeffct10

# The Most Efficient State in US
dfusmaxeffst = df.groupby(['State'], as_index=False).sum('Employees')
dfusmaxeffst.insert(3,'Efficiency',1)
dfusmaxeffst['Efficiency'] = dfusmaxeffst['Revenue (rounded)'] / dfusmaxeffst['Employees']
dfusmaxeffst = dfusmaxeffst.sort_values('Efficiency', ascending=False).reset_index(drop=True)
dfusmaxeffst10 = dfusmaxeffst.head(10)
dfusmaxeffst

dfusmaxeffst10 = dfusmaxeffst10.merge(df[['State','City','Company','Zip Code']], how = 'inner', on = ['State'])

gdfstsusmaxeffst10 = gdfsts.merge(dfusmaxeffst10, how = 'inner', on = ['State'])
gdfusactusmaxeffst10 = gdfusact.merge(dfusmaxeffst10, how = 'inner', on = ['Zip Code']).sort_values('Efficiency', ascending=False).reset_index(drop=True)
gdfusactusmaxeffst10

musmaxeffst10 = gdfstsusmaxeffst10.explore(column='Efficiency', name='The Most Efficient State in US visualized by State', cmap='coolwarm', edgecolor='black', marker_kwds={'radius': 5})
gdfusactusmaxeffst10.explore(m=musmaxeffst10, column='Efficiency', name='The Most Efficient State in US visualized by City', cmap='RdYlGn', edgecolor='black', marker_kwds={'radius': 5})
folium.LayerControl().add_to(musmaxeffst10)
musmaxeffst10

	State	region	postal	latitude	longitude	geometry	Company	Industry	City	Zip Code	Website	Employees	Revenue (rounded)	CEO
0	Minnesota	Midwest	MN	46	-93	POLYGON ((-89.95766 47.28691, -90.13175 47.292...	UnitedHealth	Health Care: Insurance and Managed Care	Eden Prairie	55344	unitedhealthgroup.com	400000	400,300,000,000	Stephen Hemsley
1	Minnesota	Midwest	MN	46	-93	POLYGON ((-89.95766 47.28691, -90.13175 47.292...	Target	General Merchandisers	Minneapolis	55403	target.com	440000	106,600,000,000	Brian Cornell

	Zip Code	Latitude	Longitude	Population
0	00601	18	-67	16,721
1	00602	18	-67	37,510

	State	region	postal	latitude	longitude	geometry
0	Minnesota	Midwest	MN	46	-93	POLYGON ((-89.95766 47.28691, -90.13175 47.292...
1	Montana	West	MT	47	-110	POLYGON ((-116.04823 49.00037, -113.0595 49.00...
2	North Dakota	Midwest	ND	47	-100	POLYGON ((-97.22894 49.00089, -97.21414 48.902...
4	Idaho	West	ID	44	-114	POLYGON ((-116.04823 49.00037, -115.9678 47.95...
5	Washington	West	WA	47	-120	POLYGON ((-117.03143 48.99931, -117.02665 47.7...
6	Arizona	West	AZ	34	-112	POLYGON ((-109.04522 36.99991, -109.04367 31.3...
7	California	West	CA	37	-120	POLYGON ((-114.64222 35.05311, -114.62212 34.9...
8	Colorado	West	CO	39	-106	POLYGON ((-102.05017 40.00081, -102.04012 38.4...
9	Nevada	West	NV	39	-117	POLYGON ((-117.02825 42.00002, -114.03422 41.9...
10	New Mexico	West	NM	35	-106	POLYGON ((-109.04367 31.3419, -109.04522 36.99...
11	Oregon	West	OR	44	-120	POLYGON ((-116.915 45.99998, -116.679 45.80736...
12	Utah	West	UT	40	-112	POLYGON ((-114.03422 41.99312, -111.05024 42.0...
13	Wyoming	West	WY	43	-108	POLYGON ((-111.08518 44.50615, -111.06719 44.5...
14	Arkansas	South	AR	35	-92	POLYGON ((-89.66292 36.02307, -89.67351 35.94,...
15	Iowa	Midwest	IA	42	-93	POLYGON ((-96.45266 43.50179, -95.35994 43.500...
16	Kansas	Midwest	KS	38	-98	POLYGON ((-102.04118 36.99198, -102.04012 38.4...
17	Missouri	Midwest	MO	39	-92	POLYGON ((-89.66292 36.02307, -90.31539 36.023...
18	Nebraska	Midwest	NE	42	-100	POLYGON ((-102.05017 40.00081, -102.05017 40.0...
19	Oklahoma	South	OK	35	-97	POLYGON ((-103.00322 36.99516, -102.04118 36.9...
20	South Dakota	Midwest	SD	44	-100	POLYGON ((-96.53945 46.01797, -96.55689 45.872...
21	Louisiana	South	LA	31	-92	POLYGON ((-94.05976 33.01212, -93.09403 33.010...
22	Texas	South	TX	31	-99	POLYGON ((-106.50734 31.75429, -106.61953 31.9...
23	Connecticut	Northeast	CT	42	-73	POLYGON ((-73.49794 42.05451, -72.73222 42.035...
24	Massachusetts	Northeast	MA	42	-72	POLYGON ((-71.80091 42.01325, -72.73222 42.035...
25	New Hampshire	Northeast	NH	44	-72	POLYGON ((-70.81505 42.86519, -70.9336 42.8842...
26	Rhode Island	Northeast	RI	42	-72	POLYGON ((-71.85383 41.32004, -71.79295 41.466...
27	Vermont	Northeast	VT	44	-73	POLYGON ((-72.45707 42.72708, -73.28203 42.743...
28	Alabama	South	AL	33	-87	POLYGON ((-88.16696 34.99967, -86.90968 34.999...
29	Florida	South	FL	28	-82	POLYGON ((-87.53039 30.2742, -87.45789 30.4112...
30	Georgia	South	GA	33	-83	POLYGON ((-85.00519 30.99069, -85.05442 31.108...
31	Mississippi	South	MS	33	-90	POLYGON ((-91.15624 33.01, -91.10808 33.20684,...
32	South Carolina	South	SC	34	-81	POLYGON ((-80.86501 32.03316, -81.03644 32.084...
33	Illinois	Midwest	IL	40	-89	POLYGON ((-91.43033 40.3686, -91.41023 40.5511...
34	Indiana	Midwest	IN	40	-86	POLYGON ((-88.05108 37.8196, -88.01881 38.0217...
35	Kentucky	South	KY	37	-86	POLYGON ((-89.49836 36.5062, -89.27398 36.6115...
36	North Carolina	South	NC	36	-79	POLYGON ((-83.07637 34.97903, -84.32097 34.986...
37	Ohio	Midwest	OH	40	-83	POLYGON ((-84.82368 39.10653, -84.81787 39.799...
38	Tennessee	South	TN	36	-86	POLYGON ((-90.24924 35.02083, -90.13493 35.113...
39	Virginia	South	VA	38	-78	MULTIPOLYGON (((-83.33059 36.67266, -83.17851 ...
40	Wisconsin	Midwest	WI	44	-90	POLYGON ((-91.2282 43.50125, -91.25466 43.6139...
41	West Virginia	South	WV	39	-81	POLYGON ((-81.97254 37.53595, -82.16728 37.554...
42	Delaware	South	DE	39	-75	POLYGON ((-75.04839 38.44876, -75.71462 38.449...
43	District of Columbia	South	DC	39	-77	POLYGON ((-77.04124 38.78954, -77.04123 38.789...
44	Maryland	South	MD	39	-77	POLYGON ((-75.37754 38.01538, -75.37754 38.015...
45	New Jersey	Northeast	NJ	40	-74	POLYGON ((-75.52781 39.49865, -75.55427 39.691...
46	New York	Northeast	NY	43	-75	POLYGON ((-73.49794 42.05451, -73.55349 41.289...
47	Pennsylvania	Northeast	PA	41	-78	POLYGON ((-80.51893 40.64111, -80.51627 42.324...
48	Maine	Northeast	ME	45	-69	POLYGON ((-70.64573 43.09008, -70.75102 43.080...
49	Michigan	Midwest	MI	43	-85	POLYGON ((-89.95766 47.28691, -89.84283 47.464...

	State	region	postal	latitude	longitude	geometry	Revenue (rounded)
0	Texas	South	TX	31	-99	POLYGON ((-106.50734 31.75429, -106.61953 31.9...	2,680,800,000,000
1	California	West	CA	37	-120	POLYGON ((-114.64222 35.05311, -114.62212 34.9...	2,399,300,000,000
2	New York	Northeast	NY	43	-75	POLYGON ((-73.49794 42.05451, -73.55349 41.289...	2,085,200,000,000
3	Washington	West	WA	47	-120	POLYGON ((-117.03143 48.99931, -117.02665 47.7...	1,299,400,000,000
4	Illinois	Midwest	IL	40	-89	POLYGON ((-91.43033 40.3686, -91.41023 40.5511...	1,040,200,000,000
5	Ohio	Midwest	OH	40	-83	POLYGON ((-84.82368 39.10653, -84.81787 39.799...	1,035,800,000,000
6	Minnesota	Midwest	MN	46	-93	POLYGON ((-89.95766 47.28691, -90.13175 47.292...	802,600,000,000
7	Arkansas	South	AR	35	-92	POLYGON ((-89.66292 36.02307, -89.67351 35.94,...	764,300,000,000
8	Virginia	South	VA	38	-78	MULTIPOLYGON (((-83.33059 36.67266, -83.17851 ...	738,400,000,000
9	Pennsylvania	Northeast	PA	41	-78	POLYGON ((-80.51893 40.64111, -80.51627 42.324...	672,400,000,000
10	Michigan	Midwest	MI	43	-85	POLYGON ((-89.95766 47.28691, -89.84283 47.464...	602,400,000,000
11	Georgia	South	GA	33	-83	POLYGON ((-85.00519 30.99069, -85.05442 31.108...	548,300,000,000
12	Connecticut	Northeast	CT	42	-73	POLYGON ((-73.49794 42.05451, -72.73222 42.035...	532,100,000,000
13	North Carolina	South	NC	36	-79	POLYGON ((-83.07637 34.97903, -84.32097 34.986...	469,300,000,000
14	Florida	South	FL	28	-82	POLYGON ((-87.53039 30.2742, -87.45789 30.4112...	448,700,000,000
15	New Jersey	Northeast	NJ	40	-74	POLYGON ((-75.52781 39.49865, -75.55427 39.691...	440,900,000,000
16	Rhode Island	Northeast	RI	42	-72	POLYGON ((-71.85383 41.32004, -71.79295 41.466...	440,300,000,000
17	Nebraska	Midwest	NE	42	-100	POLYGON ((-102.05017 40.00081, -102.05017 40.0...	427,000,000,000
18	Massachusetts	Northeast	MA	42	-72	POLYGON ((-71.80091 42.01325, -72.73222 42.035...	384,500,000,000
19	Indiana	Midwest	IN	40	-86	POLYGON ((-88.05108 37.8196, -88.01881 38.0217...	320,500,000,000
20	Tennessee	South	TN	36	-86	POLYGON ((-90.24924 35.02083, -90.13493 35.113...	312,700,000,000
21	Missouri	Midwest	MO	39	-92	POLYGON ((-89.66292 36.02307, -90.31539 36.023...	262,600,000,000
22	District of Columbia	South	DC	39	-77	POLYGON ((-77.04124 38.78954, -77.04123 38.789...	185,200,000,000
23	Wisconsin	Midwest	WI	44	-90	POLYGON ((-91.2282 43.50125, -91.25466 43.6139...	144,900,000,000
24	Kentucky	South	KY	37	-86	POLYGON ((-89.49836 36.5062, -89.27398 36.6115...	136,600,000,000
25	Arizona	West	AZ	34	-112	POLYGON ((-109.04522 36.99991, -109.04367 31.3...	125,000,000,000
26	Maryland	South	MD	39	-77	POLYGON ((-75.37754 38.01538, -75.37754 38.015...	119,700,000,000
27	Colorado	West	CO	39	-106	POLYGON ((-102.05017 40.00081, -102.04012 38.4...	117,000,000,000
28	Idaho	West	ID	44	-114	POLYGON ((-116.04823 49.00037, -115.9678 47.95...	104,300,000,000
29	Oregon	West	OR	44	-120	POLYGON ((-116.915 45.99998, -116.679 45.80736...	88,000,000,000
30	Oklahoma	South	OK	35	-97	POLYGON ((-103.00322 36.99516, -102.04118 36.9...	48,100,000,000
31	Nevada	West	NV	39	-117	POLYGON ((-117.02825 42.00002, -114.03422 41.9...	39,800,000,000
32	Iowa	Midwest	IA	42	-93	POLYGON ((-96.45266 43.50179, -95.35994 43.500...	31,000,000,000
33	Louisiana	South	LA	31	-92	POLYGON ((-94.05976 33.01212, -93.09403 33.010...	25,000,000,000
34	Alabama	South	AL	33	-87	POLYGON ((-88.16696 34.99967, -86.90968 34.999...	16,800,000,000
35	Delaware	South	DE	39	-75	POLYGON ((-75.04839 38.44876, -75.71462 38.449...	12,400,000,000
36	Kansas	Midwest	KS	38	-98	POLYGON ((-102.04118 36.99198, -102.04012 38.4...	9,100,000,000

	Employees	Revenue (rounded)
State
California	2857700	2,399,300,000,000
New York	2825500	2,085,200,000,000
Washington	2750600	1,299,400,000,000
Texas	2432800	2,680,800,000,000
Arkansas	2283200	764,300,000,000
Illinois	1655400	1,040,200,000,000
Virginia	1461200	738,400,000,000
Georgia	1327300	548,300,000,000
Minnesota	1269900	802,600,000,000
Ohio	1255100	1,035,800,000,000
Tennessee	1190500	312,700,000,000
Florida	1058700	448,700,000,000
New Jersey	944300	440,900,000,000
Pennsylvania	929200	672,400,000,000
North Carolina	856900	469,300,000,000
Massachusetts	854000	384,500,000,000
Michigan	838000	602,400,000,000
Connecticut	806700	532,100,000,000
Nebraska	463100	427,000,000,000
Rhode Island	344900	440,300,000,000
Indiana	336600	320,500,000,000
Missouri	305400	262,600,000,000
Maryland	290200	119,700,000,000
Idaho	244600	104,300,000,000
Wisconsin	196500	144,900,000,000
Arizona	194000	125,000,000,000
Colorado	194000	117,000,000,000
Nevada	159100	39,800,000,000
Kentucky	134400	136,600,000,000
Oregon	109600	88,000,000,000
District of Columbia	93200	185,200,000,000
Iowa	52800	31,000,000,000
Louisiana	37300	25,000,000,000
Alabama	31600	16,800,000,000
Delaware	24000	12,400,000,000
Kansas	14000	9,100,000,000
Oklahoma	13300	48,100,000,000

The 500 largest companies in the USA Data Analysis¶

Draw the Map of Cities in Fortune500 Dataset¶

Statistics in State Level¶

Statistics in City Level¶

Statistics in Industry Level¶

	Company	Industry	City	State	Zip Code	Website	Employees	Revenue (rounded)	CEO
0	Walmart	General Merchandisers	Bentonville	Arkansas	72712	walmart.com	2,100,000	681,000,000,000.00	Douglas McMillon
1	Amazon	Internet Services and Retailing	Seattle	Washington	98109	amazon.com	1,556,000	638,000,000,000.00	Andrew Jassy

	State	Company	Industry	City	Zip Code	Revenue (rounded)
0	Oregon	Lithia Motors	Automotive Retailing, Services	Medford	97501	36,600,000,000
1	Idaho	Micron Technology	Semiconductors and Other Electronic Components	Boise	83716	25,100,000,000
2	Maryland	Constellation Energy	Energy	Baltimore	21231	23,600,000,000
3	Iowa	Casey's General Stores	Specialty Retailers: Other	Ankeny	50021	14,900,000,000
4	Nebraska	Mutual of Omaha Insurance	Insurance: Life, Health (Mutual)	Omaha	68175	14,600,000,000
5	Delaware	DuPont	Chemicals	Wilmington	19805	12,400,000,000
6	Arkansas	J.B. Hunt Transport Services	Trucking, Truck Leasing	Lowell	72745	12,100,000,000
7	Louisiana	Entergy	Utilities: Gas and Electric	New Orleans	70113	11,900,000,000
8	Nevada	Las Vegas Sands	Hotels, Casinos, Resorts	Las Vegas	89113	11,300,000,000
9	Nevada	Caesars Entertainment	Hotels, Casinos, Resorts	Reno	89113	11,300,000,000
10	North Carolina	Advance Auto Parts	Specialty Retailers: Other	Raleigh	27609	10,900,000,000
11	Washington	Expeditors International of Washington	Transportation and Logistics	Bellevue	98006	10,600,000,000
12	Washington	Lululemon athletica	Specialty Retailers: Apparel	Sumner	98006	10,600,000,000
13	Oklahoma	Williams	Pipelines	Tulsa	74172	10,500,000,000
14	Rhode Island	FM	Insurance: Property and Casualty (Stock)	Johnston	2919	10,400,000,000
15	Massachusetts	Analog Devices	Semiconductors and Other Electronic Components	Wilmington	1887	9,400,000,000
16	Tennessee	Eastman Chemical	Chemicals	Kingsport	37660	9,400,000,000
17	New Jersey	Zoetis	Pharmaceuticals	Parsippany	7054	9,300,000,000
18	Colorado	Ovintiv	Mining, Crude-Oil Production	Denver	80202	9,200,000,000
19	Kansas	Seaboard	Food Production	Merriam	66202	9,100,000,000
20	District of Columbia	Xylem	Industrial Machinery	Washington	20003	8,600,000,000
21	Wisconsin	Rockwell Automation	Electronics, Electrical Equip.	Milwaukee	53204	8,300,000,000
22	Connecticut	XPO	Transportation and Logistics	Greenwich	6831	8,100,000,000
23	New York	Foot Locker	Specialty Retailers: Apparel	New York	10169	8,000,000,000
24	New York	Voya Financial	Diversified Financials	New York	10169	8,000,000,000
25	Ohio	TransDigm	Aerospace & Defense	Cleveland	44115	7,900,000,000
26	Texas	KBR	Information Technology Services	Houston	77002	7,700,000,000
27	Indiana	Zimmer Biomet s	Medical Products and Equipment	Warsaw	46580	7,700,000,000
28	Florida	Watsco	Wholesalers: Diversified	Miami	33133	7,600,000,000
29	Georgia	Newell Brands	Home Equipment, Furnishings	Atlanta	30328	7,600,000,000
30	Arizona	Microchip Technology	Semiconductors and Other Electronic Components	Chandler	85224	7,600,000,000
31	Minnesota	Fastenal	Wholesalers: Diversified	Winona	55987	7,500,000,000
32	California	Monster Beverage	Beverages	Corona	92879	7,500,000,000
33	California	Endeavor	Entertainment	Beverly Hills	92879	7,500,000,000
34	Kentucky	Yum Brands	Food Services	Louisville	40213	7,500,000,000
35	Virginia	Science Applications International	Information Technology Services	Reston	20190	7,500,000,000
36	Michigan	CMS Energy	Utilities: Gas and Electric	Jackson	49201	7,500,000,000
37	Pennsylvania	Howmet Aerospace	Aerospace & Defense	Pittsburgh	15212	7,400,000,000
38	Missouri	Core & Main	Wholesalers: Diversified	St. Louis	63146	7,400,000,000
39	Alabama	Vulcan Materials	Building Materials, Glass	Birmingham	35242	7,400,000,000
40	Illinois	Ingredion	Food Production	Westchester	60154	7,400,000,000

	Revenue (rounded)
State
Arkansas	191,075,000,000
Washington	118,127,272,727
Nebraska	106,750,000,000
Rhode Island	88,060,000,000
District of Columbia	61,733,333,333
Idaho	52,150,000,000
Texas	49,644,444,444
Minnesota	47,211,764,706
Kentucky	45,533,333,333
Oregon	44,000,000,000
North Carolina	42,663,636,364
California	41,367,241,379
Michigan	40,160,000,000
Indiana	40,062,500,000
Maryland	39,900,000,000
New York	39,343,396,226
Ohio	36,992,857,143
Connecticut	35,473,333,333
Pennsylvania	35,389,473,684
Missouri	32,825,000,000
Illinois	32,506,250,000
Georgia	32,252,941,176
Virginia	29,536,000,000
New Jersey	29,393,333,333
Tennessee	28,427,272,727
Massachusetts	24,031,250,000
Florida	20,395,454,545
Wisconsin	18,112,500,000
Oklahoma	16,033,333,333
Iowa	15,500,000,000
Colorado	14,625,000,000
Arizona	13,888,888,889
Nevada	13,266,666,667
Louisiana	12,500,000,000
Delaware	12,400,000,000
Kansas	9,100,000,000
Alabama	8,400,000,000

	Employees
State
Arkansas	570,800
Washington	250,055
Idaho	122,300
Nebraska	115,775
Tennessee	108,227
Maryland	96,733
Georgia	78,076
North Carolina	77,900
Minnesota	74,700
Rhode Island	68,980
New Jersey	62,953
Virginia	58,448
Michigan	55,867
Oregon	54,800
Connecticut	53,780
Massachusetts	53,375
New York	53,311
Nevada	53,033
Illinois	51,731
California	49,271
Pennsylvania	48,905
Florida	48,123
Texas	45,052
Ohio	44,825
Kentucky	44,800
Indiana	42,075
Missouri	38,175
District of Columbia	31,067
Iowa	26,400
Wisconsin	24,562
Colorado	24,250
Delaware	24,000
Arizona	21,556
Louisiana	18,650
Alabama	15,800
Kansas	14,000
Oklahoma	4,433

	State	City	Company	Industry	Zip Code	Efficiency
0	New York	New York	StoneX	Diversified Financials	10017	22,200,000
1	California	El Segundo	A-Mark Precious Metals	Wholesalers: Diversified	95014	19,400,000
2	District of Columbia	Washington	Fannie Mae	Diversified Financials	20005	18,621,951
3	Virginia	McLean	Freddie Mac	Diversified Financials	22102	15,074,074
4	Texas	San Antonio	Valero Energy	Petroleum Refining	77389	12,525,253
5	Ohio	Toledo	Welltower	Real Estate	43017	11,428,571
6	Florida	Miami	World Kinect	Energy	33811	8,978,723
7	New Jersey	Parsippany	PBF Energy	Petroleum Refining	8933	8,487,179
8	Oklahoma	Oklahoma City	Devon Energy	Mining, Crude-Oil Production	74103	6,913,043
9	Pennsylvania	Conshohocken	Cencora	Wholesalers: Health Care	19428	6,681,818
10	Tennessee	Brentwood	Delek US	Petroleum Refining	38120	6,250,000
11	Colorado	Denver	Ovintiv	Mining, Crude-Oil Production	80112	5,750,000
12	Missouri	Chesterfield	Reinsurance of America	Insurance: Life, Health (Stock)	63105	5,390,244
13	Wisconsin	Milwaukee	Northwestern Mutual	Insurance: Life, Health (Mutual)	53202	5,048,780
14	Massachusetts	Waltham	Global Partners	Energy	1701	4,526,316
15	Minnesota	Inver Grove Heights	CHS	Food Production	55344	3,672,897
16	Connecticut	Bloomfield	Cigna	Health Care: Pharmacy and Other Services	6002	3,412,983
17	Arizona	Scottsdale	Taylor Morrison Home	Homebuilders	85004	2,733,333
18	Georgia	Atlanta	Pulte	Homebuilders	30339	2,632,353
19	Nebraska	Omaha	Mutual of Omaha Insurance	Insurance: Life, Health (Mutual)	68131	2,246,154
20	Michigan	Lansing	Auto-Owners Insurance	Insurance: Property and Casualty (Mutual)	48265	2,164,384
21	Illinois	Chicago	Archer Daniels Midland	Food Production	60015	1,979,167
22	Rhode Island	Johnston	FM	Insurance: Property and Casualty (Stock)	2895	1,793,103
23	Kentucky	Louisville	Humana	Health Care: Insurance and Managed Care	40202	1,792,998
24	Indiana	Indianapolis	Elevance Health	Health Care: Insurance and Managed Care	46204	1,706,847
25	Maryland	Baltimore	Constellation Energy	Energy	20817	1,661,972
26	Arkansas	El Dorado	Murphy USA	Specialty Retailers: Other	72712	1,543,103
27	North Carolina	Charlotte	Sonic Automotive	Automotive Retailing, Services	28255	1,314,815
28	Oregon	Medford	Lithia Motors	Automotive Retailing, Services	97005	1,211,921
29	Washington	Bellevue	Paccar	Motor Vehicles & Parts	98109	1,119,601
30	Louisiana	New Orleans	Entergy	Utilities: Gas and Electric	71203	967,480
31	Iowa	Des Moines	Principal Financial	Insurance: Life, Health (Stock)	50392	817,259
32	Kansas	Merriam	Seaboard	Food Production	66202	650,000
33	Alabama	Birmingham	Vulcan Materials	Building Materials, Glass	35203	616,667
34	Idaho	Boise	Micron Technology	Semiconductors and Other Electronic Components	83706	522,917
35	Delaware	Wilmington	DuPont	Chemicals	19805	516,667
36	Nevada	Las Vegas	Las Vegas Sands	Hotels, Casinos, Resorts	89109	281,796

	State	Efficiency
0	District of Columbia	6,460,449
1	Oklahoma	4,298,822
2	Texas	2,644,095
3	Ohio	1,818,979
4	New York	1,747,529
5	Missouri	1,495,312
6	California	1,459,801
7	Colorado	1,366,474
8	Virginia	1,332,278
9	Wisconsin	1,320,243
10	Massachusetts	1,273,417
11	New Jersey	1,182,423
12	Nebraska	1,116,963
13	Pennsylvania	1,115,642
14	Florida	1,107,382
15	Rhode Island	1,088,965
16	Minnesota	1,083,344
17	Connecticut	972,484
18	Tennessee	945,176
19	Oregon	929,638
20	Arizona	929,285
21	Michigan	862,969
22	Indiana	807,850
23	Maryland	803,561
24	Kentucky	778,332
25	Louisiana	745,740
26	Georgia	742,803
27	Illinois	731,582
28	North Carolina	654,844
29	Arkansas	653,435
30	Kansas	650,000
31	Iowa	633,705
32	Washington	561,945
33	Alabama	548,129
34	Delaware	516,667
35	Idaho	462,883
36	Nevada	252,357

	City	State	Revenue (rounded)
0	New York	New York	1,820,500,000,000
1	Houston	Texas	906,500,000,000
2	Seattle	Washington	744,900,000,000
3	Bentonville	Arkansas	681,000,000,000
4	Atlanta	Georgia	489,700,000,000
5	Irving	Texas	462,000,000,000
6	Omaha	Nebraska	427,000,000,000
7	Eden Prairie	Minnesota	418,000,000,000
8	Cupertino	California	391,000,000,000
9	Spring	Texas	379,700,000,000
10	Woonsocket	Rhode Island	372,800,000,000
11	Chicago	Illinois	368,100,000,000
12	Dallas	Texas	366,500,000,000
13	Mountain View	California	366,300,000,000
14	Charlotte	North Carolina	331,300,000,000
15	San Francisco	California	296,000,000,000
16	Conshohocken	Pennsylvania	294,000,000,000
17	Cincinnati	Ohio	276,100,000,000
18	Santa Clara	California	255,600,000,000
19	Issaquah	Washington	254,500,000,000
20	Bloomfield	Connecticut	247,100,000,000
21	Redmond	Washington	245,100,000,000
22	Indianapolis	Indiana	238,900,000,000
23	Dublin	Ohio	226,800,000,000
24	Detroit	Michigan	216,300,000,000
25	Minneapolis	Minnesota	211,400,000,000
26	St. Louis	Missouri	207,500,000,000
27	McLean	Virginia	197,900,000,000
28	Washington	District of Columbia	185,200,000,000
29	Dearborn	Michigan	185,000,000,000
30	San Antonio	Texas	172,600,000,000
31	Menlo Park	California	164,500,000,000
32	Deerfield	Illinois	162,800,000,000
33	Arlington	Virginia	159,500,000,000
34	San Jose	California	153,000,000,000
35	Austin	Texas	150,700,000,000
36	Philadelphia	Pennsylvania	141,100,000,000
37	Findlay	Ohio	140,400,000,000
38	Louisville	Kentucky	136,600,000,000
39	Stamford	Connecticut	132,500,000,000
40	Richmond	Virginia	127,000,000,000
41	Memphis	Tennessee	124,800,000,000
42	Bloomington	Illinois	123,000,000,000
43	Purchase	New York	120,100,000,000
44	Pittsburgh	Pennsylvania	119,200,000,000
45	Boston	Massachusetts	106,400,000,000
46	Palo Alto	California	106,000,000,000
47	Boise	Idaho	104,300,000,000
48	Milwaukee	Wisconsin	96,700,000,000
49	Bethesda	Maryland	96,100,000,000
50	Round Rock	Texas	95,600,000,000
51	Burbank	California	91,400,000,000
52	Reston	Virginia	90,300,000,000
53	Columbus	Ohio	90,300,000,000
54	New Brunswick	New Jersey	88,800,000,000
55	Miami	Florida	85,200,000,000
56	Mooresville	North Carolina	83,700,000,000
57	Newark	New Jersey	80,700,000,000
58	Cleveland	Ohio	79,300,000,000
59	Mayfield Village	Ohio	75,400,000,000
60	Fremont	California	73,400,000,000
61	Phoenix	Arizona	73,000,000,000
62	Jacksonville	Florida	71,700,000,000
63	Nashville	Tennessee	70,600,000,000
64	San Diego	California	64,600,000,000
65	Rahway	New Jersey	64,200,000,000
66	Northbrook	Illinois	64,100,000,000
67	Armonk	New York	62,800,000,000
68	Lakeland	Florida	60,200,000,000
69	Waltham	Massachusetts	60,100,000,000
70	Providence	Rhode Island	57,100,000,000
71	Framingham	Massachusetts	56,400,000,000
72	North Chicago	Illinois	56,300,000,000
73	Springfield	Massachusetts	55,000,000,000
74	Parsippany	New Jersey	54,200,000,000
75	Fort Worth	Texas	54,200,000,000
76	Springdale	Arkansas	53,300,000,000
77	Moline	Illinois	51,700,000,000
78	Beaverton	Oregon	51,400,000,000
79	Denver	Colorado	51,200,000,000
80	St. Paul	Minnesota	48,500,000,000
81	Oakland	California	48,500,000,000
82	Princeton	New Jersey	48,300,000,000
83	Irvine	California	48,000,000,000
84	Cambridge	Massachusetts	44,600,000,000
85	Bellevue	Washington	44,300,000,000
86	St. Petersburg	Florida	43,800,000,000
87	Midland	Michigan	43,000,000,000
88	Greenwich	Connecticut	42,800,000,000
89	Abbott Park	Illinois	42,000,000,000
90	Richfield	Minnesota	41,500,000,000
91	Newport News	Virginia	41,100,000,000
92	Falls Church	Virginia	41,000,000,000
93	Long Beach	California	40,600,000,000
94	Goodlettsville	Tennessee	40,600,000,000
95	Inver Grove Heights	Minnesota	39,300,000,000
96	Los Gatos	California	39,000,000,000
97	Evendale	Ohio	38,700,000,000
98	Norwalk	Connecticut	38,300,000,000
99	Rosemont	Illinois	37,900,000,000
100	Marlborough	Massachusetts	37,200,000,000
101	Arlington	Texas	36,800,000,000
102	Medford	Oregon	36,600,000,000
103	Columbus	Indiana	34,100,000,000
104	Thousand Oaks	California	33,400,000,000
105	Tulsa	Oklahoma	32,200,000,000
106	Akron	Ohio	31,900,000,000
107	Chesapeake	Virginia	30,800,000,000
108	Beverly Hills	California	30,700,000,000
109	Bloomfield Hills	Michigan	30,500,000,000
110	Duluth	Georgia	28,900,000,000
111	Foster City	California	28,800,000,000
112	Las Vegas	Nevada	28,500,000,000
113	Centennial	Colorado	27,900,000,000
114	Brentwood	Tennessee	27,400,000,000
115	Glen Allen	Virginia	27,300,000,000
116	Newport Beach	California	27,100,000,000
117	Fort Lauderdale	Florida	26,800,000,000
118	Hartford	Connecticut	26,500,000,000
119	Westlake	Texas	26,000,000,000
120	Raleigh	North Carolina	25,900,000,000
121	Englewood	Colorado	25,800,000,000
122	Lake Forest	Illinois	25,600,000,000
123	Coral Gables	Florida	24,900,000,000
124	Juno Beach	Florida	24,800,000,000
125	Palm Beach Gardens	Florida	24,800,000,000
126	Auburn Hills	Michigan	24,500,000,000
127	Riverwoods	Illinois	23,600,000,000
128	Baltimore	Maryland	23,600,000,000
129	Southfield	Michigan	23,300,000,000
130	Tampa	Florida	22,900,000,000
131	Portage	Michigan	22,600,000,000
132	Chesterfield	Missouri	22,100,000,000
133	Scottsdale	Arizona	22,000,000,000
134	Maumee	Ohio	21,600,000,000
135	Melbourne	Florida	21,300,000,000
136	Madison	Wisconsin	21,300,000,000
137	Dublin	California	21,100,000,000
138	Vernon Hills	Illinois	21,000,000,000
139	Allentown	Pennsylvania	20,600,000,000
140	Franklin Lakes	New Jersey	20,200,000,000
141	Teaneck	New Jersey	19,700,000,000
142	Roseland	New Jersey	19,200,000,000
143	Toledo	Ohio	19,000,000,000
144	Columbus	Georgia	18,900,000,000
145	Radnor	Pennsylvania	18,400,000,000
146	Long Island City	New York	18,300,000,000
147	El Dorado	Arkansas	17,900,000,000
148	Rosemead	California	17,600,000,000
149	Fort Wayne	Indiana	17,500,000,000
150	Birmingham	Alabama	16,800,000,000
151	Springfield	Missouri	16,700,000,000
152	Benton Harbor	Michigan	16,600,000,000
153	Redwood City	California	16,300,000,000
154	Chandler	Arizona	16,300,000,000
155	Des Peres	Missouri	16,300,000,000
156	Menomonee Falls	Wisconsin	16,200,000,000
157	Arden Hills	Minnesota	16,200,000,000
158	Des Moines	Iowa	16,100,000,000
159	Oklahoma City	Oklahoma	15,900,000,000
160	Glenview	Illinois	15,900,000,000
161	King of Prussia	Pennsylvania	15,800,000,000
162	Lansing	Michigan	15,800,000,000
163	Woodland Hills	California	15,500,000,000
164	Summit	New Jersey	15,500,000,000
165	Burlington	Massachusetts	15,400,000,000
166	Durham	North Carolina	15,400,000,000
167	New Britain	Connecticut	15,400,000,000
168	Wallingford	Connecticut	15,200,000,000
169	Ankeny	Iowa	14,900,000,000
170	Canonsburg	Pennsylvania	14,700,000,000
171	Antioch	Tennessee	14,400,000,000
172	Farmington	Connecticut	14,300,000,000
173	Tarrytown	New York	14,200,000,000
174	Tempe	Arizona	13,700,000,000
175	Ashburn	Virginia	13,700,000,000
176	Buffalo	New York	13,500,000,000
177	Coraopolis	Pennsylvania	13,400,000,000
178	Erie	Pennsylvania	13,200,000,000
179	Monroe	Louisiana	13,100,000,000
180	Corning	New York	13,100,000,000
181	Burlington	North Carolina	13,000,000,000
182	Chattanooga	Tennessee	12,900,000,000
183	Melville	New York	12,700,000,000
184	Franklin	Tennessee	12,600,000,000
185	Wilmington	Delaware	12,400,000,000
186	Evansville	Indiana	12,300,000,000
187	Westminster	Colorado	12,100,000,000
188	Lowell	Arkansas	12,100,000,000
189	Plantation	Florida	11,900,000,000
190	Austin	Minnesota	11,900,000,000
191	New Orleans	Louisiana	11,900,000,000
192	Rolling Meadows	Illinois	11,600,000,000
193	Orlando	Florida	11,400,000,000
194	Reno	Nevada	11,300,000,000
195	Plano	Texas	11,300,000,000
196	Fairfield	Ohio	11,300,000,000
197	Bolingbrook	Illinois	11,300,000,000
198	Hershey	Pennsylvania	11,200,000,000
199	Midland	Texas	11,100,000,000
200	Calhoun	Georgia	10,800,000,000
201	Fort Washington	Pennsylvania	10,800,000,000
202	Oshkosh	Wisconsin	10,700,000,000
203	Burlington	New Jersey	10,600,000,000
204	Sumner	Washington	10,600,000,000
205	Johnston	Rhode Island	10,400,000,000
206	Victor	New York	10,000,000,000
207	Elkhart	Indiana	10,000,000,000
208	Sunny Isles Beach	Florida	10,000,000,000
209	Secaucus	New Jersey	9,900,000,000
210	Milpitas	California	9,800,000,000
211	Herndon	Virginia	9,800,000,000
212	El Segundo	California	9,700,000,000
213	Newark	California	9,600,000,000
214	Camden	New Jersey	9,600,000,000
215	Byron Center	Michigan	9,500,000,000
216	Oak Brook	Illinois	9,500,000,000
217	Kingsport	Tennessee	9,400,000,000
218	Wilmington	Massachusetts	9,400,000,000
219	Merriam	Kansas	9,100,000,000
220	Estero	Florida	9,000,000,000
221	Manhattan Beach	California	9,000,000,000
222	Mentor	Ohio	8,800,000,000
223	San Mateo	California	8,500,000,000
224	Pleasanton	California	8,400,000,000
225	Sunnyvale	California	8,400,000,000
226	Downers Grove	Illinois	8,400,000,000
227	Maplewood	Minnesota	8,300,000,000
228	Orrville	Ohio	8,200,000,000
229	Westerville	Ohio	8,000,000,000
230	Livonia	Michigan	7,800,000,000
231	New Braunfels	Texas	7,800,000,000
232	Warsaw	Indiana	7,700,000,000
233	Jackson	Michigan	7,500,000,000
234	Winona	Minnesota	7,500,000,000
235	Corona	California	7,500,000,000
236	Westchester	Illinois	7,400,000,000

	Industry	State	City	Company	Zip Code	Revenue (rounded)
0	Mail, Package and Freight Delivery	Tennessee	Memphis	FedEx	38120	87,700,000,000
1	Scientific, Photographic and Control Equipment	Massachusetts	Waltham	Thermo Fisher Scientific	02451	42,900,000,000
2	Wholesalers: Electronics and Office Equipment	Arizona	Phoenix	Avnet	85034	23,800,000,000
3	Tobacco	Virginia	Richmond	Altria	23230	20,400,000,000
4	Energy	Texas	Irving	Vistra	75039	17,200,000,000
5	Energy	Massachusetts	Waltham	Global Partners	02453	17,200,000,000
6	Waste Management	Arizona	Phoenix	Republic Services	85054	16,000,000,000
7	Household and Personal Products	New Jersey	Summit	Kenvue	07901	15,500,000,000
8	General Merchandisers	Washington	Seattle	Nordstrom	98101	15,000,000,000
9	Insurance: Property and Casualty (Mutual)	Pennsylvania	Erie	Erie Insurance	16530	13,200,000,000
10	Computers, Office Equipment	California	San Jose	Western Digital	95119	13,000,000,000
11	Health Care: Medical Facilities	Tennessee	Franklin	Community Health Systems	37067	12,600,000,000
12	Engineering & Construction	Florida	Coral Gables	MasTec	33134	12,300,000,000
13	Railroads	Georgia	Atlanta	Norfolk Southern	30308	12,100,000,000
14	Trucking, Truck Leasing	Arkansas	Lowell	J.B. Hunt Transport Services	72745	12,100,000,000
15	Hotels, Casinos, Resorts	Virginia	McLean	Hilton	22102	11,200,000,000
16	Insurance: Life, Health (Mutual)	Minnesota	Minneapolis	Thrivent Financial for Lutherans	55415	10,900,000,000
17	Wholesalers: Health Care	Virginia	Glen Allen	Owens & Minor	23060	10,700,000,000
18	Advertising, Marketing	New York	New York	Interpublic	10022	10,700,000,000
19	Construction and Farm Machinery	Wisconsin	Oshkosh	Oshkosh	54902	10,700,000,000
20	Pipelines	Oklahoma	Tulsa	Williams	74172	10,500,000,000
21	Financial Data Services	Georgia	Atlanta	Global Payments	30326	10,100,000,000
22	Publishing, Printing	New York	New York	News Corp.	10036	10,100,000,000
23	Internet Services and Retailing	Colorado	Englewood	QVC	80112	10,000,000,000
24	Motor Vehicles & Parts	Indiana	Elkhart	THOR Industries	46514	10,000,000,000
25	Health Care: Pharmacy and Other Services	New Jersey	Secaucus	Quest Diagnostics	07094	9,900,000,000
26	Wholesalers: Food and Grocery	Michigan	Byron Center	SpartanNash	49315	9,500,000,000
27	Chemicals	Tennessee	Kingsport	Eastman Chemical	37660	9,400,000,000
28	Airlines	New York	Long Island City	JetBlue Airways	11101	9,300,000,000
29	Pharmaceuticals	New Jersey	Parsippany	Zoetis	07054	9,300,000,000
30	Commercial Banks	Ohio	Cleveland	KeyCorp	44114	9,200,000,000
31	Health Care: Insurance and Managed Care	New York	New York	Oscar Health	10013	9,200,000,000
32	Mining, Crude-Oil Production	Colorado	Denver	Ovintiv	80202	9,200,000,000
33	Telecommunications	New York	Long Island City	Altice USA	11101	9,000,000,000
34	Oil and Gas Equipment, Services	Texas	Houston	NOV	77042	8,900,000,000
35	Apparel	New York	New York	PVH	10017	8,700,000,000
36	Securities	California	San Mateo	Franklin Resources	94403	8,500,000,000
37	Industrial Machinery	Illinois	Downers Grove	Dover	60515	8,400,000,000
38	Computer Software	California	Pleasanton	Workday	94588	8,400,000,000
39	Diversified Outsourcing Services	New York	New York	ABM Industries	10006	8,400,000,000
40	Packaging, Containers	Illinois	Lake Forest	Packaging Corp. of America	60045	8,400,000,000
41	Insurance: Property and Casualty (Stock)	Illinois	Chicago	Old Republic International	60601	8,200,000,000
42	Homebuilders	Arizona	Scottsdale	Taylor Morrison Home	85251	8,200,000,000
43	Insurance: Life, Health (Stock)	Minnesota	St. Paul	Securian Financial	55101	8,200,000,000
44	Transportation and Logistics	Connecticut	Greenwich	XPO	06831	8,100,000,000
45	Petroleum Refining	Texas	Houston	Par Pacific	77024	8,000,000,000
46	Electronics, Electrical Equip.	Ohio	Westerville	Vertiv s	43082	8,000,000,000
47	Real Estate	Ohio	Toledo	Welltower	43615	8,000,000,000
48	Network and Other Communications Equipment	California	Santa Clara	Palo Alto Networks	95054	8,000,000,000
49	Specialty Retailers: Apparel	New York	New York	Foot Locker	10001	8,000,000,000
50	Diversified Financials	New York	New York	Voya Financial	10169	8,000,000,000
51	Metals	Texas	Irving	Commercial Metals	75039	7,900,000,000
52	Food Consumer Products	Missouri	St. Louis	Post s	63144	7,900,000,000
53	Automotive Retailing, Services	Texas	New Braunfels	Rush Enterprises	78130	7,800,000,000
54	Medical Products and Equipment	Indiana	Warsaw	Zimmer Biomet s	46580	7,700,000,000
55	Food & Drug Stores	Arizona	Phoenix	Sprouts Farmers Market	85054	7,700,000,000
56	Home Equipment, Furnishings	Georgia	Atlanta	Newell Brands	30328	7,600,000,000
57	Semiconductors and Other Electronic Components	Arizona	Chandler	Microchip Technology	85224	7,600,000,000
58	Semiconductors and Other Electronic Components	California	San Jose	Sanmina	95134	7,600,000,000
59	Specialty Retailers: Other	Virginia	Richmond	ARKO	23227	7,600,000,000
60	Information Technology Services	Virginia	Reston	Science Applications International	20190	7,500,000,000
61	Entertainment	California	Beverly Hills	Endeavor	90210	7,500,000,000
62	Utilities: Gas and Electric	Michigan	Jackson	CMS Energy	49201	7,500,000,000
63	Beverages	California	Corona	Monster Beverage	92879	7,500,000,000
64	Food Services	Kentucky	Louisville	Yum Brands	40213	7,500,000,000
65	Wholesalers: Diversified	Missouri	St. Louis	Core & Main	63146	7,400,000,000
66	Building Materials, Glass	Alabama	Birmingham	Vulcan Materials	35242	7,400,000,000
67	Aerospace & Defense	Pennsylvania	Pittsburgh	Howmet Aerospace	15212	7,400,000,000
68	Food Production	Illinois	Westchester	Ingredion	60154	7,400,000,000

	Industry	Revenue (rounded)
0	Internet Services and Retailing	1,330,100,000,000
1	Commercial Banks	1,321,000,000,000
2	General Merchandisers	1,116,800,000,000
3	Petroleum Refining	1,044,500,000,000
4	Health Care: Insurance and Managed Care	908,000,000,000
5	Wholesalers: Health Care	853,200,000,000
6	Insurance: Property and Casualty (Stock)	842,900,000,000
7	Health Care: Pharmacy and Other Services	669,500,000,000
8	Diversified Financials	627,600,000,000
9	Computers, Office Equipment	598,300,000,000
10	Motor Vehicles & Parts	590,800,000,000
11	Specialty Retailers: Other	505,200,000,000
12	Pharmaceuticals	487,300,000,000
13	Telecommunications	473,800,000,000
14	Semiconductors and Other Electronic Components	446,900,000,000
15	Food & Drug Stores	441,900,000,000
16	Aerospace & Defense	407,400,000,000
17	Computer Software	393,200,000,000
18	Utilities: Gas and Electric	329,200,000,000
19	Insurance: Life, Health (Stock)	299,200,000,000
20	Pipelines	268,400,000,000
21	Food Consumer Products	263,800,000,000
22	Entertainment	259,900,000,000
23	Insurance: Life, Health (Mutual)	249,800,000,000
24	Insurance: Property and Casualty (Mutual)	226,100,000,000
25	Food Production	222,800,000,000
26	Airlines	221,400,000,000
27	Automotive Retailing, Services	215,700,000,000
28	Wholesalers: Food and Grocery	211,900,000,000
29	Mining, Crude-Oil Production	210,700,000,000
30	Industrial Machinery	207,800,000,000
31	Chemicals	203,000,000,000
32	Information Technology Services	201,900,000,000
33	Medical Products and Equipment	184,600,000,000
34	Mail, Package and Freight Delivery	178,800,000,000
35	Securities	175,900,000,000
36	Financial Data Services	175,300,000,000
37	Wholesalers: Diversified	169,600,000,000
38	Energy	163,200,000,000
39	Wholesalers: Electronics and Office Equipment	158,200,000,000
40	Household and Personal Products	155,300,000,000
41	Construction and Farm Machinery	138,900,000,000
42	Health Care: Medical Facilities	132,500,000,000
43	Specialty Retailers: Apparel	121,800,000,000
44	Homebuilders	119,800,000,000
45	Metals	116,600,000,000
46	Food Services	103,600,000,000
47	Engineering & Construction	100,000,000,000
48	Real Estate	95,100,000,000
49	Beverages	91,600,000,000
50	Diversified Outsourcing Services	89,500,000,000
51	Network and Other Communications Equipment	87,800,000,000
52	Packaging, Containers	80,800,000,000
53	Apparel	79,600,000,000
54	Hotels, Casinos, Resorts	76,100,000,000
55	Transportation and Logistics	60,700,000,000
56	Oil and Gas Equipment, Services	59,600,000,000
57	Tobacco	58,300,000,000
58	Railroads	50,800,000,000
59	Electronics, Electrical Equip.	46,000,000,000
60	Scientific, Photographic and Control Equipment	42,900,000,000
61	Waste Management	38,100,000,000
62	Building Materials, Glass	34,800,000,000
63	Advertising, Marketing	26,400,000,000
64	Home Equipment, Furnishings	26,200,000,000
65	Trucking, Truck Leasing	12,100,000,000
66	Publishing, Printing	10,100,000,000

	Revenue (rounded)
Industry
Wholesalers: Health Care	170,640,000,000
General Merchandisers	159,542,857,143
Health Care: Insurance and Managed Care	151,333,333,333
Petroleum Refining	116,055,555,556
Health Care: Pharmacy and Other Services	111,583,333,333
Internet Services and Retailing	102,315,384,615
Computers, Office Equipment	99,716,666,667
Mail, Package and Freight Delivery	89,400,000,000
Food & Drug Stores	88,380,000,000
Telecommunications	67,685,714,286
Commercial Banks	66,050,000,000
Motor Vehicles & Parts	59,080,000,000
Computer Software	56,171,428,571
Insurance: Property and Casualty (Stock)	46,827,777,778
Insurance: Property and Casualty (Mutual)	45,220,000,000
Scientific, Photographic and Control Equipment	42,900,000,000
Wholesalers: Food and Grocery	42,380,000,000
Diversified Financials	41,840,000,000
Wholesalers: Electronics and Office Equipment	39,550,000,000
Pharmaceuticals	37,484,615,385
Aerospace & Defense	37,036,363,636
Airlines	36,900,000,000
Construction and Farm Machinery	34,725,000,000
Pipelines	33,550,000,000
Semiconductors and Other Electronic Components	31,921,428,571
Food Production	31,828,571,429
Specialty Retailers: Other	31,575,000,000
Insurance: Life, Health (Mutual)	31,225,000,000
Household and Personal Products	31,060,000,000
Tobacco	29,150,000,000
Entertainment	28,877,777,778
Energy	27,200,000,000
Insurance: Life, Health (Stock)	27,200,000,000
Health Care: Medical Facilities	26,500,000,000
Food Consumer Products	21,983,333,333
Network and Other Communications Equipment	21,950,000,000
Financial Data Services	21,912,500,000
Mining, Crude-Oil Production	21,070,000,000
Specialty Retailers: Apparel	20,300,000,000
Homebuilders	19,966,666,667
Apparel	19,900,000,000
Oil and Gas Equipment, Services	19,866,666,667
Automotive Retailing, Services	19,609,090,909
Waste Management	19,050,000,000
Industrial Machinery	18,890,909,091
Medical Products and Equipment	18,460,000,000
Beverages	18,320,000,000
Securities	17,590,000,000
Food Services	17,266,666,667
Railroads	16,933,333,333
Chemicals	16,916,666,667
Information Technology Services	16,825,000,000
Engineering & Construction	16,666,666,667
Metals	16,657,142,857
Real Estate	15,850,000,000
Utilities: Gas and Electric	15,676,190,476
Hotels, Casinos, Resorts	15,220,000,000
Diversified Outsourcing Services	14,916,666,667
Wholesalers: Diversified	14,133,333,333
Advertising, Marketing	13,200,000,000
Transportation and Logistics	12,140,000,000
Trucking, Truck Leasing	12,100,000,000
Building Materials, Glass	11,600,000,000
Packaging, Containers	11,542,857,143
Electronics, Electrical Equip.	11,500,000,000
Publishing, Printing	10,100,000,000
Home Equipment, Furnishings	8,733,333,333

	OBJECTID	postalCode	PO_NAME	STATE	borough	ST_FIPS	CTY_FIPS	Shape_Leng	Shape_Area	@id	geometry
0	1	11372	Jackson Heights	NY	Queens	36	081	20,625	20,163,284	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.86942 40.74916, -73.89507 40.746...
1	2	11004	Glen Oaks	NY	Queens	36	081	23,003	22,606,527	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.71068 40.75004, -73.70869 40.748...
2	3	11040	New Hyde Park	NY	Queens	36	081	15,749	6,269,333	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.70098 40.7389, -73.70309 40.7445...
3	4	11426	Bellerose	NY	Queens	36	081	35,933	49,418,364	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.7227 40.75373, -73.72251 40.7533...
4	5	11365	Fresh Meadows	NY	Queens	36	081	38,694	69,385,866	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.81089 40.72717, -73.81116 40.728...
5	6	11373	Elmhurst	NY	Queens	36	081	33,756	42,659,400	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.88722 40.72753, -73.88723 40.728...
6	7	11001	Floral Park	NY	Queens	36	081	13,595	9,155,180	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.70098 40.7389, -73.6996 40.73959...
7	8	11375	Forest Hills	NY	Queens	36	081	36,277	55,587,772	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.85625 40.73672, -73.85205 40.737...
8	9	11427	Queens Village	NY	Queens	36	081	31,232	39,568,339	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.74169 40.73682, -73.73568 40.738...
9	10	11374	Rego Park	NY	Queens	36	081	26,324	25,203,459	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.86451 40.73407, -73.85872 40.735...
10	11	11366	Fresh Meadows	NY	Queens	36	081	27,283	20,358,593	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.77011 40.73178, -73.7751 40.7308...
11	12	11423	Hollis	NY	Queens	36	081	31,340	45,095,032	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.77011 40.73178, -73.76536 40.725...
12	13	11428	Queens Village	NY	Queens	36	081	22,516	21,985,773	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.72775 40.72258, -73.72762 40.722...
13	14	11432	Jamaica	NY	Queens	36	081	33,124	60,177,576	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.80904 40.71991, -73.79288 40.725...
14	15	11379	Middle Village	NY	Queens	36	081	33,528	57,537,331	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.8719 40.72686, -73.87182 40.7267...
15	16	11429	Queens Village	NY	Queens	36	081	26,527	39,559,398	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.72848 40.72077, -73.72793 40.719...
16	17	11435	Jamaica	NY	Queens	36	081	34,404	42,105,899	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.82606 40.7154, -73.82306 40.7161...
17	18	11415	Kew Gardens	NY	Queens	36	081	21,075	16,802,280	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.82606 40.7154, -73.82348 40.7123...
18	19	11418	Richmond Hill	NY	Queens	36	081	31,854	32,025,131	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.83715 40.70796, -73.83655 40.708...
19	20	11433	Jamaica	NY	Queens	36	081	30,604	47,739,433	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.77968 40.7086, -73.77937 40.7078...
20	21	11451	Jamaica	NY	Queens	36	081	2,962	487,600	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.80157 40.70121, -73.79887 40.702...
21	22	11221	Brooklyn	NY	Brooklyn	36	047	30,948	38,580,480	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.93822 40.68389, -73.94389 40.683...
22	23	11421	Woodhaven	NY	Queens	36	081	22,224	24,314,442	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.8492 40.69827, -73.84741 40.6957...
23	24	11419	South Richmond Hill	NY	Queens	36	081	23,069	31,278,858	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.83754 40.69136, -73.82812 40.694...
24	25	11434	Jamaica	NY	Queens	36	081	43,830	87,379,704	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.76989 40.69631, -73.76605 40.692...
25	26	11216	Brooklyn	NY	Brooklyn	36	047	26,473	26,478,229	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.94564 40.69203, -73.94389 40.683...
26	27	11416	Ozone Park	NY	Queens	36	081	21,631	18,854,850	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.8443 40.68944, -73.83754 40.6913...
27	28	11233	Brooklyn	NY	Brooklyn	36	047	30,234	37,871,694	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.93822 40.68389, -73.91785 40.686...
28	29	11436	Jamaica	NY	Queens	36	081	20,579	22,699,295	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.80585 40.68291, -73.79391 40.686...
29	30	11213	Brooklyn	NY	Brooklyn	36	047	23,899	29,631,004	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.9374 40.67973, -73.92906 40.6792...
30	31	11212	Brooklyn	NY	Brooklyn	36	047	26,622	41,972,104	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.90294 40.67084, -73.90223 40.668...
31	32	11225	Brooklyn	NY	Brooklyn	36	047	23,346	23,698,630	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.95797 40.67066, -73.95505 40.670...
32	33	11218	Brooklyn	NY	Brooklyn	36	047	30,623	36,868,799	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.97208 40.6506, -73.9714 40.64826...
33	34	11226	Brooklyn	NY	Brooklyn	36	047	28,636	39,408,598	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.9619 40.65487, -73.96152 40.6550...
34	35	11219	Brooklyn	NY	Brooklyn	36	047	29,464	42,002,738	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.98906 40.64412, -73.99193 40.642...
35	36	11210	Brooklyn	NY	Brooklyn	36	047	29,975	47,887,023	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.9584 40.63633, -73.95374 40.6385...
36	37	11230	Brooklyn	NY	Brooklyn	36	047	31,795	49,926,703	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.96451 40.63669, -73.96422 40.635...
37	38	11204	Brooklyn	NY	Brooklyn	36	047	30,325	43,555,185	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.98108 40.63529, -73.98085 40.635...
38	39	10471	Bronx	NY	Bronx	36	005	53,209	89,651,407	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.88192 40.90667, -73.8779 40.9055...
39	40	10470	Bronx	NY	Bronx	36	005	32,649	21,543,462	http://nyc.pediacities.com/Resource/PostalCode...	POLYGON ((-73.8779 40.90555, -73.87786 40.9059...

	Zip Code	Latitude	Longitude	Population	geometry	City	State	Employees	Company
0	98109	48	-122	32,552	POINT (-122.34486 47.63128)	Seattle	Washington	2101600	Amazon
1	98188	47	-122	26,769	POINT (-122.27398 47.44754)	Seattle	Washington	2101600	Alaska Air
2	98101	48	-122	16,396	POINT (-122.33454 47.61119)	Seattle	Washington	2101600	Nordstrom
3	98101	48	-122	16,396	POINT (-122.33454 47.61119)	Seattle	Washington	2101600	Coupang
4	98134	48	-122	779	POINT (-122.33686 47.57691)	Seattle	Washington	2101600	Starbucks
5	98119	48	-122	26,390	POINT (-122.36932 47.63943)	Seattle	Washington	2101600	Expedia
6	72712	36	-94	38,072	POINT (-94.22196 36.39837)	Bentonville	Arkansas	2100000	Walmart
7	10041	41	-74	6,217	POINT (-74.01 40.7031)	New York	New York	2028200	S&P Global
8	10006	41	-74	4,475	POINT (-74.01306 40.70949)	New York	New York	2028200	ABM Industries
9	10017	41	-74	14,985	POINT (-73.9726 40.75235)	New York	New York	2028200	Omnicom
10	10017	41	-74	14,985	POINT (-73.9726 40.75235)	New York	New York	2028200	Kyndryl s
11	10017	41	-74	14,985	POINT (-73.9726 40.75235)	New York	New York	2028200	Travelers
12	10017	41	-74	14,985	POINT (-73.9726 40.75235)	New York	New York	2028200	TIAA
13	10017	41	-74	14,985	POINT (-73.9726 40.75235)	New York	New York	2028200	JPMorgan Chase
14	10013	41	-74	28,215	POINT (-74.00472 40.72001)	New York	New York	2028200	Oscar Health
15	10013	41	-74	28,215	POINT (-74.00472 40.72001)	New York	New York	2028200	Citi
16	10010	41	-74	31,905	POINT (-73.98243 40.73899)	New York	New York	2028200	New York Life Insurance
17	10003	41	-74	53,825	POINT (-73.98915 40.73184)	New York	New York	2028200	Consolidated Edison
18	10019	41	-74	44,276	POINT (-73.9853 40.76586)	New York	New York	2028200	Apollo Global Management
19	10003	41	-74	53,825	POINT (-73.98915 40.73184)	New York	New York	2028200	Warner Bros. Discovery
20	10001	41	-74	29,079	POINT (-73.99728 40.75064)	New York	New York	2028200	Foot Locker
21	10001	41	-74	29,079	POINT (-73.99728 40.75064)	New York	New York	2028200	Guardian Life
22	10001	41	-74	29,079	POINT (-73.99728 40.75064)	New York	New York	2028200	BlackRock
23	10105	41	-74	0	POINT (-73.978 40.7644)	New York	New York	2028200	Equitable
24	10286	41	-74	5,269	POINT (-74.01182 40.7052)	New York	New York	2028200	Bank of New York
25	10001	41	-74	29,079	POINT (-73.99728 40.75064)	New York	New York	2028200	Macy's
26	10017	41	-74	14,985	POINT (-73.9726 40.75235)	New York	New York	2028200	PVH
27	10019	41	-74	44,276	POINT (-73.9853 40.76586)	New York	New York	2028200	Loews
28	10282	41	-74	5,960	POINT (-74.01495 40.71655)	New York	New York	2028200	Goldman Sachs
29	10036	41	-74	30,589	POINT (-73.99064 40.75976)	New York	New York	2028200	Marsh & McLennan
30	10169	41	-74	0	POINT (-73.97643 40.75467)	New York	New York	2028200	Voya Financial
31	10169	41	-74	0	POINT (-73.97643 40.75467)	New York	New York	2028200	StoneX
32	10154	41	-74	0	POINT (-73.97249 40.75778)	New York	New York	2028200	Blackstone
33	10153	41	-74	0	POINT (-73.97244 40.76362)	New York	New York	2028200	Estée Lauder
34	10036	41	-74	30,589	POINT (-73.99064 40.75976)	New York	New York	2028200	News Corp.
35	10036	41	-74	30,589	POINT (-73.99064 40.75976)	New York	New York	2028200	Hess
36	10036	41	-74	30,589	POINT (-73.99064 40.75976)	New York	New York	2028200	Fox
37	10036	41	-74	30,589	POINT (-73.99064 40.75976)	New York	New York	2028200	Paramount Global
38	10019	41	-74	44,276	POINT (-73.9853 40.76586)	New York	New York	2028200	International Flavors & Fragrances
39	10036	41	-74	30,589	POINT (-73.99064 40.75976)	New York	New York	2028200	Morgan Stanley

	State	Industry	Revenue (rounded)
0	Texas	Petroleum Refining	858,500,000,000
1	New York	Commercial Banks	737,300,000,000
2	Washington	Internet Services and Retailing	682,000,000,000
3	Arkansas	General Merchandisers	681,000,000,000
4	California	Internet Services and Retailing	590,600,000,000
5	Michigan	Motor Vehicles & Parts	420,200,000,000
6	Minnesota	Health Care: Insurance and Managed Care	400,300,000,000
7	Rhode Island	Health Care: Pharmacy and Other Services	372,800,000,000
8	Nebraska	Insurance: Property and Casualty (Stock)	371,400,000,000
9	Pennsylvania	Wholesalers: Health Care	294,000,000,000
10	Virginia	Aerospace & Defense	247,400,000,000
11	Connecticut	Health Care: Pharmacy and Other Services	247,100,000,000
12	North Carolina	Commercial Banks	233,000,000,000
13	Ohio	Wholesalers: Health Care	226,800,000,000
14	New Jersey	Pharmaceuticals	210,600,000,000
15	Indiana	Health Care: Insurance and Managed Care	177,000,000,000
16	Missouri	Health Care: Insurance and Managed Care	163,100,000,000
17	Georgia	Specialty Retailers: Other	159,500,000,000
18	District of Columbia	Diversified Financials	152,700,000,000
19	Illinois	Food & Drug Stores	147,700,000,000
20	Kentucky	Health Care: Insurance and Managed Care	117,800,000,000
21	Tennessee	Mail, Package and Freight Delivery	87,700,000,000
22	Idaho	Food & Drug Stores	79,200,000,000
23	Maryland	Aerospace & Defense	71,000,000,000
24	Florida	Food & Drug Stores	60,200,000,000
25	Massachusetts	Specialty Retailers: Apparel	56,400,000,000
26	Oregon	Apparel	51,400,000,000
27	Wisconsin	Insurance: Life, Health (Mutual)	41,400,000,000
28	Nevada	Hotels, Casinos, Resorts	39,800,000,000
29	Oklahoma	Pipelines	32,200,000,000
30	Colorado	Mining, Crude-Oil Production	27,900,000,000
31	Arizona	Mining, Crude-Oil Production	25,500,000,000
32	Iowa	Insurance: Life, Health (Stock)	16,100,000,000
33	Louisiana	Telecommunications	13,100,000,000
34	Delaware	Chemicals	12,400,000,000
35	Alabama	Commercial Banks	9,400,000,000
36	Kansas	Food Production	9,100,000,000

	State	Industry	Company	Zip Code	Revenue (rounded)
0	Texas	Petroleum Refining	Exxon Mobil	77389	858,500,000,000
1	Texas	Petroleum Refining	Chevron	77002	858,500,000,000
2	Texas	Petroleum Refining	Phillips 66	77042	858,500,000,000
3	Texas	Petroleum Refining	Valero Energy	78249	858,500,000,000
4	Texas	Petroleum Refining	HF Sinclair	75219	858,500,000,000
5	Texas	Petroleum Refining	Par Pacific	77024	858,500,000,000
6	New York	Commercial Banks	JPMorgan Chase	10017	737,300,000,000
7	New York	Commercial Banks	Citi	10013	737,300,000,000
8	New York	Commercial Banks	Goldman Sachs	10282	737,300,000,000
9	New York	Commercial Banks	Morgan Stanley	10036	737,300,000,000
10	New York	Commercial Banks	Bank of New York	10286	737,300,000,000
11	New York	Commercial Banks	M&T Bank	14203	737,300,000,000
12	Washington	Internet Services and Retailing	Amazon	98109	682,000,000,000
13	Washington	Internet Services and Retailing	Coupang	98101	682,000,000,000
14	Washington	Internet Services and Retailing	Expedia	98119	682,000,000,000
15	Arkansas	General Merchandisers	Walmart	72712	681,000,000,000
16	California	Internet Services and Retailing	Alphabet	94043	590,600,000,000
17	California	Internet Services and Retailing	Meta	94025	590,600,000,000
18	California	Internet Services and Retailing	Uber	94158	590,600,000,000
19	California	Internet Services and Retailing	Airbnb	94103	590,600,000,000
20	California	Internet Services and Retailing	DoorDash	94107	590,600,000,000
21	California	Internet Services and Retailing	Ebay	95125	590,600,000,000
22	Michigan	Motor Vehicles & Parts	General Motors	48265	420,200,000,000
23	Michigan	Motor Vehicles & Parts	Ford Motor	48126	420,200,000,000
24	Michigan	Motor Vehicles & Parts	Lear	48033	420,200,000,000
25	Michigan	Motor Vehicles & Parts	BorgWarner	48326	420,200,000,000
26	Michigan	Motor Vehicles & Parts	Autoliv	48326	420,200,000,000
27	Minnesota	Health Care: Insurance and Managed Care	UnitedHealth	55344	400,300,000,000
28	Rhode Island	Health Care: Pharmacy and Other Services	CVS Health	02895	372,800,000,000
29	Nebraska	Insurance: Property and Casualty (Stock)	Berkshire Hathaway	68131	371,400,000,000
30	Pennsylvania	Wholesalers: Health Care	Cencora	19428	294,000,000,000

	State	Industry	Company	Zip Code	Revenue (rounded)
0	Nevada	Hotels, Casinos, Resorts	MGM Resorts International	89109	39,800,000,000
1	Nevada	Hotels, Casinos, Resorts	Caesars Entertainment	89501	39,800,000,000
2	Nevada	Hotels, Casinos, Resorts	Las Vegas Sands	89113	39,800,000,000
3	Oregon	Automotive Retailing, Services	Lithia Motors	97501	36,600,000,000
4	Idaho	Semiconductors and Other Electronic Components	Micron Technology	83716	25,100,000,000
5	Maryland	Energy	Constellation Energy	21231	23,600,000,000
6	Oklahoma	Mining, Crude-Oil Production	Devon Energy	73102	15,900,000,000
7	Iowa	Specialty Retailers: Other	Casey's General Stores	50021	14,900,000,000
8	Nebraska	Insurance: Life, Health (Mutual)	Mutual of Omaha Insurance	68175	14,600,000,000
9	North Carolina	Automotive Retailing, Services	Sonic Automotive	28211	14,200,000,000
10	Delaware	Chemicals	DuPont	19805	12,400,000,000
11	Arkansas	Trucking, Truck Leasing	J.B. Hunt Transport Services	72745	12,100,000,000
12	Louisiana	Utilities: Gas and Electric	Entergy	70113	11,900,000,000
13	Virginia	Homebuilders	NVR	20190	10,700,000,000
14	Washington	Specialty Retailers: Apparel	Lululemon athletica	98390	10,600,000,000
15	Rhode Island	Insurance: Property and Casualty (Stock)	FM	02919	10,400,000,000
16	Colorado	Internet Services and Retailing	QVC	80112	10,000,000,000
17	New Jersey	Food Consumer Products	Campbell's	08103	9,600,000,000
18	Tennessee	Chemicals	Eastman Chemical	37660	9,400,000,000
19	Connecticut	Securities	Interactive Brokers	06830	9,400,000,000
20	Massachusetts	Semiconductors and Other Electronic Components	Analog Devices	01887	9,400,000,000
21	Kansas	Food Production	Seaboard	66202	9,100,000,000
22	Georgia	Packaging, Containers	Graphic Packaging	30328	8,800,000,000
23	District of Columbia	Industrial Machinery	Xylem	20003	8,600,000,000
24	Illinois	Packaging, Containers	Packaging Corp. of America	60045	8,400,000,000
25	Wisconsin	Electronics, Electrical Equip.	Rockwell Automation	53204	8,300,000,000
26	New York	Specialty Retailers: Apparel	Foot Locker	10001	8,000,000,000
27	Ohio	Electronics, Electrical Equip.	Vertiv s	43082	8,000,000,000
28	Missouri	Food Consumer Products	Post s	63144	7,900,000,000
29	Michigan	Home Equipment, Furnishings	Masco	48152	7,800,000,000
30	Indiana	Medical Products and Equipment	Zimmer Biomet s	46580	7,700,000,000
31	Texas	Information Technology Services	KBR	77002	7,700,000,000
32	Arizona	Semiconductors and Other Electronic Components	Microchip Technology	85224	7,600,000,000
33	Florida	Wholesalers: Diversified	Watsco	33133	7,600,000,000
34	California	Beverages	Monster Beverage	92879	7,500,000,000
35	Minnesota	Wholesalers: Diversified	Fastenal	55987	7,500,000,000
36	Kentucky	Food Services	Yum Brands	40213	7,500,000,000
37	Pennsylvania	Aerospace & Defense	Howmet Aerospace	15212	7,400,000,000
38	Alabama	Building Materials, Glass	Vulcan Materials	35242	7,400,000,000

	Zip Code	Latitude	Longitude	Population	geometry	State	Industry	Company	Revenue (rounded)
0	89501	40	-120	3,641	POINT (-119.81262 39.52602)	Nevada	Hotels, Casinos, Resorts	Caesars Entertainment	39,800,000,000
1	89113	36	-115	37,313	POINT (-115.26236 36.06017)	Nevada	Hotels, Casinos, Resorts	Las Vegas Sands	39,800,000,000
2	89109	36	-115	6,924	POINT (-115.16327 36.12603)	Nevada	Hotels, Casinos, Resorts	MGM Resorts International	39,800,000,000
3	97501	42	-123	45,008	POINT (-122.89459 42.2856)	Oregon	Automotive Retailing, Services	Lithia Motors	36,600,000,000
4	83716	44	-116	21,091	POINT (-115.6548 43.67271)	Idaho	Semiconductors and Other Electronic Components	Micron Technology	25,100,000,000
5	21231	39	-77	14,556	POINT (-76.592 39.28775)	Maryland	Energy	Constellation Energy	23,600,000,000
6	73102	35	-98	3,590	POINT (-97.5191 35.47065)	Oklahoma	Mining, Crude-Oil Production	Devon Energy	15,900,000,000
7	50021	42	-94	29,718	POINT (-93.56838 41.7207)	Iowa	Specialty Retailers: Other	Casey's General Stores	14,900,000,000
8	68175	41	-96	0	POINT (-95.96584 41.26502)	Nebraska	Insurance: Life, Health (Mutual)	Mutual of Omaha Insurance	14,600,000,000
9	28211	35	-81	32,637	POINT (-80.79604 35.16816)	North Carolina	Automotive Retailing, Services	Sonic Automotive	14,200,000,000
10	19805	40	-76	40,315	POINT (-75.59373 39.74523)	Delaware	Chemicals	DuPont	12,400,000,000
11	72745	36	-94	14,521	POINT (-94.10046 36.2477)	Arkansas	Trucking, Truck Leasing	J.B. Hunt Transport Services	12,100,000,000
12	70113	30	-90	9,888	POINT (-90.08382 29.9422)	Louisiana	Utilities: Gas and Electric	Entergy	11,900,000,000
13	20190	39	-77	22,092	POINT (-77.33806 38.95968)	Virginia	Homebuilders	NVR	10,700,000,000
14	98390	47	-122	11,497	POINT (-122.22894 47.21195)	Washington	Specialty Retailers: Apparel	Lululemon athletica	10,600,000,000
15	02919	42	-72	29,473	POINT (-71.52002 41.82733)	Rhode Island	Insurance: Property and Casualty (Stock)	FM	10,400,000,000
16	80112	40	-105	36,687	POINT (-104.85854 39.57288)	Colorado	Internet Services and Retailing	QVC	10,000,000,000
17	08103	40	-75	13,137	POINT (-75.11275 39.93533)	New Jersey	Food Consumer Products	Campbell's	9,600,000,000
18	06830	41	-74	24,919	POINT (-73.624 41.048)	Connecticut	Securities	Interactive Brokers	9,400,000,000
19	01887	43	-71	23,195	POINT (-71.16541 42.56091)	Massachusetts	Semiconductors and Other Electronic Components	Analog Devices	9,400,000,000
20	37660	37	-83	40,114	POINT (-82.57339 36.52938)	Tennessee	Chemicals	Eastman Chemical	9,400,000,000
21	66202	39	-95	17,321	POINT (-94.66909 39.02392)	Kansas	Food Production	Seaboard	9,100,000,000
22	30328	34	-84	38,821	POINT (-84.38617 33.93194)	Georgia	Packaging, Containers	Graphic Packaging	8,800,000,000
23	20003	39	-77	36,194	POINT (-76.99033 38.88193)	District of Columbia	Industrial Machinery	Xylem	8,600,000,000
24	60045	42	-88	20,957	POINT (-87.87006 42.23807)	Illinois	Packaging, Containers	Packaging Corp. of America	8,400,000,000
25	53204	43	-88	40,008	POINT (-87.92552 43.01806)	Wisconsin	Electronics, Electrical Equip.	Rockwell Automation	8,300,000,000
26	43082	40	-83	33,799	POINT (-82.88437 40.15537)	Ohio	Electronics, Electrical Equip.	Vertiv s	8,000,000,000
27	10001	41	-74	29,079	POINT (-73.99728 40.75064)	New York	Specialty Retailers: Apparel	Foot Locker	8,000,000,000
28	63144	39	-90	9,580	POINT (-90.3482 38.61943)	Missouri	Food Consumer Products	Post s	7,900,000,000
29	48152	42	-83	30,394	POINT (-83.37433 42.42597)	Michigan	Home Equipment, Furnishings	Masco	7,800,000,000
30	46580	41	-86	21,812	POINT (-85.87212 41.20884)	Indiana	Medical Products and Equipment	Zimmer Biomet s	7,700,000,000
31	77002	30	-95	19,616	POINT (-95.36538 29.75635)	Texas	Information Technology Services	KBR	7,700,000,000
32	85224	33	-112	48,304	POINT (-111.87616 33.32351)	Arizona	Semiconductors and Other Electronic Components	Microchip Technology	7,600,000,000
33	33133	26	-80	34,651	POINT (-80.24327 25.72983)	Florida	Wholesalers: Diversified	Watsco	7,600,000,000
34	55987	44	-92	33,911	POINT (-91.63143 43.98469)	Minnesota	Wholesalers: Diversified	Fastenal	7,500,000,000
35	92879	34	-118	48,756	POINT (-117.5357 33.87979)	California	Beverages	Monster Beverage	7,500,000,000
36	40213	38	-86	15,805	POINT (-85.71571 38.177)	Kentucky	Food Services	Yum Brands	7,500,000,000
37	15212	40	-80	27,600	POINT (-80.00844 40.47106)	Pennsylvania	Aerospace & Defense	Howmet Aerospace	7,400,000,000
38	35242	33	-87	56,956	POINT (-86.67073 33.42371)	Alabama	Building Materials, Glass	Vulcan Materials	7,400,000,000

	State	Industry	Employees
0	Nevada	Hotels, Casinos, Resorts	159,100
1	Idaho	Semiconductors and Other Electronic Components	48,000
2	Kentucky	Food Services	31,700
3	Oregon	Automotive Retailing, Services	30,200
4	Delaware	Chemicals	24,000
5	Iowa	Insurance: Life, Health (Stock)	19,700
6	Washington	Transportation and Logistics	18,900
7	Illinois	Information Technology Services	15,100
8	Maryland	Energy	14,200
9	Kansas	Food Production	14,000
10	Colorado	Telecommunications	13,700
11	Indiana	Metals	13,000
12	Louisiana	Utilities: Gas and Electric	12,300
13	Alabama	Building Materials, Glass	12,000
14	Arkansas	Specialty Retailers: Other	11,600
15	North Carolina	Automotive Retailing, Services	10,800
16	District of Columbia	Diversified Financials	8,200
17	Michigan	Insurance: Property and Casualty (Mutual)	7,300
18	Wisconsin	Utilities: Gas and Electric	7,000
19	Georgia	Homebuilders	6,800
20	Nebraska	Insurance: Life, Health (Mutual)	6,500
21	Virginia	Tobacco	6,200
22	Rhode Island	Insurance: Property and Casualty (Stock)	5,800
23	Texas	Diversified Financials	5,200
24	Pennsylvania	Homebuilders	4,900
25	Florida	Energy	4,700
26	Massachusetts	Real Estate	4,700
27	Missouri	Insurance: Life, Health (Stock)	4,100
28	Minnesota	Insurance: Life, Health (Mutual)	4,000
29	New Jersey	Petroleum Refining	3,900
30	Connecticut	Securities	3,000
31	Arizona	Homebuilders	3,000
32	Oklahoma	Mining, Crude-Oil Production	2,300
33	Tennessee	Petroleum Refining	2,000
34	New York	Mining, Crude-Oil Production	1,800
35	Ohio	Real Estate	700
36	California	Wholesalers: Diversified	500

	Industry	Employees
0	General Merchandisers	3101200
1	Internet Services and Retailing	2071800
2	Commercial Banks	1577000
3	Specialty Retailers: Other	1558200
4	Information Technology Services	1478900
5	Food & Drug Stores	1148100
6	Food Services	1109800
7	Aerospace & Defense	911500
8	Motor Vehicles & Parts	893000
9	Mail, Package and Freight Delivery	794300
10	Insurance: Property and Casualty (Stock)	789100
11	Specialty Retailers: Apparel	669500
12	Health Care: Insurance and Managed Care	650300
13	Semiconductors and Other Electronic Components	627900
14	Food Consumer Products	604700
15	Industrial Machinery	594700
16	Health Care: Medical Facilities	573200
17	Health Care: Pharmacy and Other Services	572800
18	Telecommunications	566700
19	Computer Software	565200
20	Diversified Outsourcing Services	564900
21	Pharmaceuticals	549400
22	Medical Products and Equipment	501600
23	Hotels, Casinos, Resorts	495100
24	Airlines	467900
25	Computers, Office Equipment	447700
26	Chemicals	379700
27	Diversified Financials	350100
28	Entertainment	338000
29	Utilities: Gas and Electric	306200
30	Real Estate	273800
31	Financial Data Services	260100
32	Wholesalers: Diversified	255100
33	Household and Personal Products	255000
34	Network and Other Communications Equipment	251700
35	Transportation and Logistics	251400
36	Food Production	241400
37	Engineering & Construction	240500
38	Automotive Retailing, Services	232000
39	Construction and Farm Machinery	210900
40	Packaging, Containers	191400
41	Wholesalers: Health Care	188600
42	Wholesalers: Food and Grocery	186100
43	Insurance: Life, Health (Stock)	178200
44	Securities	172200
45	Petroleum Refining	160600
46	Electronics, Electrical Equip.	158300
47	Metals	140900
48	Apparel	140500
49	Oil and Gas Equipment, Services	139000
50	Beverages	131200
51	Advertising, Marketing	128200
52	Scientific, Photographic and Control Equipment	125000
53	Energy	121900
54	Insurance: Property and Casualty (Mutual)	113800
55	Waste Management	103700
56	Tobacco	89300
57	Mining, Crude-Oil Production	89000
58	Wholesalers: Electronics and Office Equipment	88900
59	Home Equipment, Furnishings	83600
60	Railroads	75500
61	Insurance: Life, Health (Mutual)	70800
62	Building Materials, Glass	66000
63	Pipelines	55200
64	Homebuilders	49800
65	Trucking, Truck Leasing	33600
66	Publishing, Printing	23900

	Zip Code	Latitude	Longitude	Population	geometry	Industry	Employees	State	Company
0	01752	42	-72	41,398	POINT (-71.54681 42.3494)	General Merchandisers	3101200	Massachusetts	BJ's Wholesale Club
1	55403	45	-93	17,354	POINT (-93.28588 44.97023)	General Merchandisers	3101200	Minnesota	Target
2	98027	48	-122	28,335	POINT (-122.00176 47.50387)	General Merchandisers	3101200	Washington	Costco
3	72712	36	-94	38,072	POINT (-94.22196 36.39837)	General Merchandisers	3101200	Arkansas	Walmart
4	98101	48	-122	16,396	POINT (-122.33454 47.61119)	General Merchandisers	3101200	Washington	Nordstrom
5	10001	41	-74	29,079	POINT (-73.99728 40.75064)	General Merchandisers	3101200	New York	Macy's
6	53051	43	-88	39,000	POINT (-88.12348 43.14905)	General Merchandisers	3101200	Wisconsin	Kohl's
7	94103	38	-122	33,524	POINT (-122.41136 37.77299)	Internet Services and Retailing	2071800	California	Airbnb
8	94043	37	-122	32,042	POINT (-122.06892 37.41188)	Internet Services and Retailing	2071800	California	Alphabet
9	98101	48	-122	16,396	POINT (-122.33454 47.61119)	Internet Services and Retailing	2071800	Washington	Coupang
10	98109	48	-122	32,552	POINT (-122.34486 47.63128)	Internet Services and Retailing	2071800	Washington	Amazon
11	94025	37	-122	40,230	POINT (-122.1829 37.45087)	Internet Services and Retailing	2071800	California	Meta
12	98119	48	-122	26,390	POINT (-122.36932 47.63943)	Internet Services and Retailing	2071800	Washington	Expedia
13	94158	38	-122	11,206	POINT (-122.39153 37.77116)	Internet Services and Retailing	2071800	California	Uber
14	33322	26	-80	40,522	POINT (-80.27429 26.14997)	Internet Services and Retailing	2071800	Florida	Chewy
15	95125	37	-122	53,934	POINT (-121.89408 37.29554)	Internet Services and Retailing	2071800	California	Ebay
16	02116	42	-71	22,040	POINT (-71.07638 42.35105)	Internet Services and Retailing	2071800	Massachusetts	Wayfair
17	80112	40	-105	36,687	POINT (-104.85854 39.57288)	Internet Services and Retailing	2071800	Colorado	QVC
18	06854	41	-73	31,753	POINT (-73.42972 41.09148)	Internet Services and Retailing	2071800	Connecticut	Booking s
19	94107	38	-122	30,190	POINT (-122.39508 37.76473)	Internet Services and Retailing	2071800	California	DoorDash
20	44114	42	-82	7,472	POINT (-81.67625 41.5137)	Commercial Banks	1577000	Ohio	KeyCorp
21	60603	42	-88	1,126	POINT (-87.6274 41.88018)	Commercial Banks	1577000	Illinois	Northern Trust
22	94104	38	-122	478	POINT (-122.40211 37.79144)	Commercial Banks	1577000	California	Wells Fargo
23	28202	35	-81	16,855	POINT (-80.8447 35.22773)	Commercial Banks	1577000	North Carolina	Truist Financial
24	02903	42	-71	12,309	POINT (-71.41003 41.81902)	Commercial Banks	1577000	Rhode Island	Citizens Financial
25	02114	42	-71	13,853	POINT (-71.06686 42.36337)	Commercial Banks	1577000	Massachusetts	State Street
26	27609	36	-79	35,548	POINT (-78.63282 35.8437)	Commercial Banks	1577000	North Carolina	First Citizens BancShares
27	45263	39	-84	0	POINT (-84.4569 39.1616)	Commercial Banks	1577000	Ohio	Fifth Third Bancorp
28	10013	41	-74	28,215	POINT (-74.00472 40.72001)	Commercial Banks	1577000	New York	Citi
29	43287	40	-83	905,748	POINT (-83.0011 39.9619)	Commercial Banks	1577000	Ohio	Huntington Bancshares
30	10017	41	-74	14,985	POINT (-73.9726 40.75235)	Commercial Banks	1577000	New York	JPMorgan Chase
31	22102	39	-77	28,577	POINT (-77.22901 38.95199)	Commercial Banks	1577000	Virginia	Capital One
32	35203	34	-87	3,301	POINT (-86.80999 33.51847)	Commercial Banks	1577000	Alabama	Regions Financial
33	10286	41	-74	5,269	POINT (-74.01182 40.7052)	Commercial Banks	1577000	New York	Bank of New York
34	28255	35	-81	0	POINT (-80.8434 35.2267)	Commercial Banks	1577000	North Carolina	Bank of America
35	10036	41	-74	30,589	POINT (-73.99064 40.75976)	Commercial Banks	1577000	New York	Morgan Stanley
36	10282	41	-74	5,960	POINT (-74.01495 40.71655)	Commercial Banks	1577000	New York	Goldman Sachs
37	14203	43	-79	2,526	POINT (-78.86507 42.86244)	Commercial Banks	1577000	New York	M&T Bank
38	15222	40	-80	5,649	POINT (-79.9912 40.44998)	Commercial Banks	1577000	Pennsylvania	PNC
39	55402	45	-93	760	POINT (-93.27119 44.97608)	Commercial Banks	1577000	Minnesota	U.S. Bancorp
40	50021	42	-94	29,718	POINT (-93.56838 41.7207)	Specialty Retailers: Other	1558200	Iowa	Casey's General Stores
41	55423	45	-93	36,779	POINT (-93.28144 44.87663)	Specialty Retailers: Other	1558200	Minnesota	Best Buy
42	60440	42	-88	52,074	POINT (-88.07572 41.70125)	Specialty Retailers: Other	1558200	Illinois	Ulta Beauty
43	65802	37	-93	46,005	POINT (-93.35167 37.21048)	Specialty Retailers: Other	1558200	Missouri	O'Reilly Automotive
44	94109	38	-122	54,397	POINT (-122.42138 37.79334)	Specialty Retailers: Other	1558200	California	Williams-Sonoma
45	28117	36	-81	48,170	POINT (-80.89658 35.57405)	Specialty Retailers: Other	1558200	North Carolina	Lowe's
46	38103	35	-90	14,025	POINT (-90.05518 35.15308)	Specialty Retailers: Other	1558200	Tennessee	AutoZone
47	23320	37	-76	59,626	POINT (-76.21807 36.75208)	Specialty Retailers: Other	1558200	Virginia	Dollar Tree
48	06902	41	-74	72,915	POINT (-73.54751 41.05949)	Specialty Retailers: Other	1558200	Connecticut	United Rentals
49	15108	41	-80	42,782	POINT (-80.20031 40.50006)	Specialty Retailers: Other	1558200	Pennsylvania	Dick's Sporting Goods
50	37072	36	-87	32,344	POINT (-86.74494 36.35686)	Specialty Retailers: Other	1558200	Tennessee	Dollar General
51	23227	38	-77	25,574	POINT (-77.44234 37.61486)	Specialty Retailers: Other	1558200	Virginia	ARKO
52	71730	33	-93	29,187	POINT (-92.63458 33.20303)	Specialty Retailers: Other	1558200	Arkansas	Murphy USA
53	27609	36	-79	35,548	POINT (-78.63282 35.8437)	Specialty Retailers: Other	1558200	North Carolina	Advance Auto Parts
54	37027	36	-87	61,961	POINT (-86.78439 35.99978)	Specialty Retailers: Other	1558200	Tennessee	Tractor Supply
55	30339	34	-84	34,872	POINT (-84.46483 33.86786)	Specialty Retailers: Other	1558200	Georgia	Home Depot
56	94560	38	-122	47,145	POINT (-122.02523 37.50771)	Information Technology Services	1478900	California	Concentrix
57	77002	30	-95	19,616	POINT (-95.36538 29.75635)	Information Technology Services	1478900	Texas	KBR
58	07666	41	-74	41,499	POINT (-74.01067 40.88998)	Information Technology Services	1478900	New Jersey	Cognizant Technology
59	10017	41	-74	14,985	POINT (-73.9726 40.75235)	Information Technology Services	1478900	New York	Kyndryl s
60	60061	42	-88	27,883	POINT (-87.96046 42.2334)	Information Technology Services	1478900	Illinois	CDW
61	10504	41	-74	7,853	POINT (-73.70629 41.13065)	Information Technology Services	1478900	New York	IBM
62	20147	39	-77	67,007	POINT (-77.47958 39.04151)	Information Technology Services	1478900	Virginia	DXC Technology
63	22102	39	-77	28,577	POINT (-77.22901 38.95199)	Information Technology Services	1478900	Virginia	Booz Allen Hamilton
64	20190	39	-77	22,092	POINT (-77.33806 38.95968)	Information Technology Services	1478900	Virginia	Leidos s
65	20190	39	-77	22,092	POINT (-77.33806 38.95968)	Information Technology Services	1478900	Virginia	CACI International
66	85286	33	-112	49,870	POINT (-111.83156 33.27146)	Information Technology Services	1478900	Arizona	Insight Enterprises
67	20190	39	-77	22,092	POINT (-77.33806 38.95968)	Information Technology Services	1478900	Virginia	Science Applications International
68	83706	44	-116	36,183	POINT (-116.19427 43.59108)	Food & Drug Stores	1148100	Idaho	Albertsons
69	85054	34	-112	10,159	POINT (-111.94763 33.67515)	Food & Drug Stores	1148100	Arizona	Sprouts Farmers Market
70	45202	39	-85	17,022	POINT (-84.50243 39.10885)	Food & Drug Stores	1148100	Ohio	Kroger
71	33811	28	-82	28,456	POINT (-82.01866 27.97933)	Food & Drug Stores	1148100	Florida	Publix Super Markets
72	60015	42	-88	27,720	POINT (-87.87486 42.17239)	Food & Drug Stores	1148100	Illinois	Walgreens
73	92660	34	-118	34,788	POINT (-117.87504 33.63375)	Food Services	1109800	California	Chipotle Mexican Grill
74	98134	48	-122	779	POINT (-122.33686 47.57691)	Food Services	1109800	Washington	Starbucks
75	32837	28	-81	49,889	POINT (-81.41998 28.38074)	Food Services	1109800	Florida	Darden Restaurants
76	60607	42	-88	30,197	POINT (-87.65175 41.87467)	Food Services	1109800	Illinois	McDonald's
77	40213	38	-86	15,805	POINT (-85.71571 38.177)	Food Services	1109800	Kentucky	Yum Brands
78	75074	33	-97	53,146	POINT (-96.67304 33.03298)	Food Services	1109800	Texas	Yum China s
79	23607	37	-76	23,028	POINT (-76.42235 36.98753)	Aerospace & Defense	911500	Virginia	Huntington Ingalls Industries
80	22202	39	-77	27,352	POINT (-77.05175 38.8569)	Aerospace & Defense	911500	Virginia	Boeing
81	45215	39	-84	30,806	POINT (-84.46221 39.23536)	Aerospace & Defense	911500	Ohio	General Electric
82	44115	41	-82	10,389	POINT (-81.66999 41.49211)	Aerospace & Defense	911500	Ohio	TransDigm
83	02903	42	-71	12,309	POINT (-71.41003 41.81902)	Aerospace & Defense	911500	Rhode Island	Textron
84	15212	40	-80	27,600	POINT (-80.00844 40.47106)	Aerospace & Defense	911500	Pennsylvania	Howmet Aerospace
85	32919	28	-81	0	POINT (-80.6386 28.0909)	Aerospace & Defense	911500	Florida	L3Harris Technologies
86	20190	39	-77	22,092	POINT (-77.33806 38.95968)	Aerospace & Defense	911500	Virginia	General Dynamics
87	20817	39	-77	37,738	POINT (-77.14919 38.99809)	Aerospace & Defense	911500	Maryland	Lockheed Martin
88	22042	39	-77	34,631	POINT (-77.19406 38.86463)	Aerospace & Defense	911500	Virginia	Northrop Grumman
89	22209	39	-77	13,725	POINT (-77.07309 38.89413)	Aerospace & Defense	911500	Virginia	RTX
90	44316	41	-81	0	POINT (-81.47083 41.07854)	Motor Vehicles & Parts	893000	Ohio	Goodyear Tire & Rubber
91	78725	30	-98	10,810	POINT (-97.60859 30.23554)	Motor Vehicles & Parts	893000	Texas	Tesla
92	48033	42	-83	16,900	POINT (-83.28812 42.46478)	Motor Vehicles & Parts	893000	Michigan	Lear
93	46514	42	-86	41,480	POINT (-85.97547 41.72328)	Motor Vehicles & Parts	893000	Indiana	THOR Industries
94	98004	48	-122	39,435	POINT (-122.20548 47.61865)	Motor Vehicles & Parts	893000	Washington	Paccar
95	48326	43	-83	24,488	POINT (-83.2531 42.67536)	Motor Vehicles & Parts	893000	Michigan	Autoliv
96	48326	43	-83	24,488	POINT (-83.2531 42.67536)	Motor Vehicles & Parts	893000	Michigan	BorgWarner
97	48126	42	-83	52,075	POINT (-83.18678 42.32999)	Motor Vehicles & Parts	893000	Michigan	Ford Motor
98	43537	42	-84	29,639	POINT (-83.68542 41.57433)	Motor Vehicles & Parts	893000	Ohio	Dana
99	48265	42	-83	4,503	POINT (-83.0456 42.3317)	Motor Vehicles & Parts	893000	Michigan	General Motors
100	30328	34	-84	38,821	POINT (-84.38617 33.93194)	Mail, Package and Freight Delivery	794300	Georgia	UPS
101	38120	35	-90	13,249	POINT (-89.85237 35.12344)	Mail, Package and Freight Delivery	794300	Tennessee	FedEx

	State	Industry	Efficiency
0	Texas	Petroleum Refining	43,606,030
1	New York	Diversified Financials	28,300,270
2	California	Wholesalers: Diversified	19,400,000
3	District of Columbia	Diversified Financials	18,621,951
4	Virginia	Diversified Financials	15,074,074
5	Ohio	Real Estate	11,428,571
6	Florida	Energy	8,978,723
7	New Jersey	Petroleum Refining	8,487,179
8	Oklahoma	Mining, Crude-Oil Production	6,913,043
9	Pennsylvania	Wholesalers: Health Care	6,681,818
10	Colorado	Mining, Crude-Oil Production	6,592,342
11	Tennessee	Petroleum Refining	6,250,000
12	Missouri	Insurance: Life, Health (Stock)	5,390,244
13	Wisconsin	Insurance: Life, Health (Mutual)	5,048,780
14	Massachusetts	Energy	4,980,743
15	Minnesota	Food Production	3,672,897
16	Connecticut	Health Care: Pharmacy and Other Services	3,412,983
17	Michigan	Motor Vehicles & Parts	2,907,881
18	Arizona	Homebuilders	2,733,333
19	Illinois	Food Production	2,639,881
20	Georgia	Homebuilders	2,632,353
21	North Carolina	Commercial Banks	2,450,342
22	Nebraska	Insurance: Life, Health (Mutual)	2,246,154
23	Rhode Island	Insurance: Property and Casualty (Stock)	1,793,103
24	Kentucky	Health Care: Insurance and Managed Care	1,792,998
25	Indiana	Health Care: Insurance and Managed Care	1,706,847
26	Maryland	Energy	1,661,972
27	Washington	Internet Services and Retailing	1,559,276
28	Arkansas	Specialty Retailers: Other	1,543,103
29	Oregon	Automotive Retailing, Services	1,211,921
30	Louisiana	Utilities: Gas and Electric	967,480
31	Iowa	Insurance: Life, Health (Stock)	817,259
32	Nevada	Hotels, Casinos, Resorts	757,071
33	Kansas	Food Production	650,000
34	Alabama	Building Materials, Glass	616,667
35	Idaho	Semiconductors and Other Electronic Components	522,917
36	Delaware	Chemicals	516,667

	Zip Code	Latitude	Longitude	Population	geometry	State	Industry	Efficiency	Company
0	77042	30	-96	40,725	POINT (-95.56015 29.74125)	Texas	Petroleum Refining	43,606,030	Phillips 66
1	78249	30	-99	61,772	POINT (-98.614 29.56752)	Texas	Petroleum Refining	43,606,030	Valero Energy
2	77389	30	-96	44,545	POINT (-95.50752 30.11509)	Texas	Petroleum Refining	43,606,030	Exxon Mobil
3	75219	33	-97	25,001	POINT (-96.81315 32.81087)	Texas	Petroleum Refining	43,606,030	HF Sinclair
4	77002	30	-95	19,616	POINT (-95.36538 29.75635)	Texas	Petroleum Refining	43,606,030	Chevron
5	77024	30	-96	37,647	POINT (-95.50869 29.77073)	Texas	Petroleum Refining	43,606,030	Par Pacific
6	10285	41	-74	0	POINT (-74.01492 40.71201)	New York	Diversified Financials	28,300,270	American Express
7	10036	41	-74	30,589	POINT (-73.99064 40.75976)	New York	Diversified Financials	28,300,270	Marsh & McLennan
8	10154	41	-74	0	POINT (-73.97249 40.75778)	New York	Diversified Financials	28,300,270	Blackstone
9	10169	41	-74	0	POINT (-73.97643 40.75467)	New York	Diversified Financials	28,300,270	StoneX
10	10169	41	-74	0	POINT (-73.97643 40.75467)	New York	Diversified Financials	28,300,270	Voya Financial
11	10022	41	-74	34,340	POINT (-73.96787 40.75856)	New York	Diversified Financials	28,300,270	Jefferies Financial
12	90245	34	-118	16,863	POINT (-118.40161 33.91709)	California	Wholesalers: Diversified	19,400,000	A-Mark Precious Metals
13	20005	39	-77	12,960	POINT (-77.0316 38.90443)	District of Columbia	Diversified Financials	18,621,951	Fannie Mae
14	22102	39	-77	28,577	POINT (-77.22901 38.95199)	Virginia	Diversified Financials	15,074,074	Freddie Mac
15	43615	42	-84	40,813	POINT (-83.67255 41.65045)	Ohio	Real Estate	11,428,571	Welltower
16	33178	26	-80	65,515	POINT (-80.4213 25.83578)	Florida	Energy	8,978,723	World Kinect
17	07054	41	-74	30,139	POINT (-74.40239 40.85527)	New Jersey	Petroleum Refining	8,487,179	PBF Energy
18	73102	35	-98	3,590	POINT (-97.5191 35.47065)	Oklahoma	Mining, Crude-Oil Production	6,913,043	Devon Energy
19	19428	40	-75	20,225	POINT (-75.30317 40.08011)	Pennsylvania	Wholesalers: Health Care	6,681,818	Cencora

	State	Industry	Efficiency
0	Oklahoma	Pipelines	5,983,422
1	Nevada	Hotels, Casinos, Resorts	757,071
2	Kansas	Food Production	650,000
3	Oregon	Apparel	647,355
4	Nebraska	Engineering & Construction	528,302
5	Louisiana	Telecommunications	524,000
6	Delaware	Chemicals	516,667
7	Alabama	Commercial Banks	479,592
8	Iowa	Specialty Retailers: Other	450,151
9	Rhode Island	Aerospace & Defense	402,941
10	Idaho	Food & Drug Stores	402,848
11	Michigan	Electronics, Electrical Equip.	377,273
12	District of Columbia	Industrial Machinery	373,913
13	North Carolina	Health Care: Pharmacy and Other Services	373,777
14	Arkansas	General Merchandisers	324,286
15	Indiana	Packaging, Containers	292,857
16	Wisconsin	General Merchandisers	270,000
17	Georgia	Mail, Package and Freight Delivery	244,761
18	Minnesota	General Merchandisers	242,273
19	Kentucky	Food Services	236,593
20	Arizona	Food & Drug Stores	220,000
21	Tennessee	Mail, Package and Freight Delivery	207,771
22	Ohio	Diversified Outsourcing Services	206,452
23	Missouri	Specialty Retailers: Other	195,093
24	Illinois	Food Services	172,667
25	Colorado	Health Care: Medical Facilities	168,421
26	Maryland	Hotels, Casinos, Resorts	161,935
27	Massachusetts	Specialty Retailers: Apparel	154,945
28	Connecticut	Network and Other Communications Equipment	121,600
29	Washington	Food Services	100,277
30	New York	Diversified Outsourcing Services	71,795
31	Pennsylvania	Diversified Outsourcing Services	65,242
32	Virginia	Hotels, Casinos, Resorts	61,878
33	Florida	Food Services	59,655
34	New Jersey	Information Technology Services	58,492
35	Texas	Food Services	46,029
36	California	Information Technology Services	21,333

	State	Industry	Company	Zip Code	Efficiency
0	Oklahoma	Pipelines	Oneok	74103	5,983,422
1	Oklahoma	Pipelines	Williams	74172	5,983,422
2	Nevada	Hotels, Casinos, Resorts	MGM Resorts International	89109	757,071
3	Nevada	Hotels, Casinos, Resorts	Las Vegas Sands	89113	757,071
4	Nevada	Hotels, Casinos, Resorts	Caesars Entertainment	89501	757,071
5	Kansas	Food Production	Seaboard	66202	650,000
6	Oregon	Apparel	Nike	97005	647,355
7	Nebraska	Engineering & Construction	Peter Kiewit Sons'	68102	528,302
8	Louisiana	Telecommunications	Lumen Technologies	71203	524,000
9	Delaware	Chemicals	DuPont	19805	516,667
10	Alabama	Commercial Banks	Regions Financial	35203	479,592
11	Iowa	Specialty Retailers: Other	Casey's General Stores	50021	450,151
12	Rhode Island	Aerospace & Defense	Textron	02903	402,941

	Zip Code	Latitude	Longitude	Population	geometry	State	Industry	Efficiency	Company
0	74103	36	-96	2,419	POINT (-95.9957 36.15617)	Oklahoma	Pipelines	5,983,422	Oneok
1	74172	36	-96	0	POINT (-95.9905 36.1544)	Oklahoma	Pipelines	5,983,422	Williams
2	89109	36	-115	6,924	POINT (-115.16327 36.12603)	Nevada	Hotels, Casinos, Resorts	757,071	MGM Resorts International
3	89113	36	-115	37,313	POINT (-115.26236 36.06017)	Nevada	Hotels, Casinos, Resorts	757,071	Las Vegas Sands
4	89501	40	-120	3,641	POINT (-119.81262 39.52602)	Nevada	Hotels, Casinos, Resorts	757,071	Caesars Entertainment
5	66202	39	-95	17,321	POINT (-94.66909 39.02392)	Kansas	Food Production	650,000	Seaboard
6	97005	45	-123	27,812	POINT (-122.80397 45.49144)	Oregon	Apparel	647,355	Nike
7	68102	41	-96	9,311	POINT (-95.93224 41.26231)	Nebraska	Engineering & Construction	528,302	Peter Kiewit Sons'
8	71203	33	-92	38,770	POINT (-92.01351 32.58398)	Louisiana	Telecommunications	524,000	Lumen Technologies
9	19805	40	-76	40,315	POINT (-75.59373 39.74523)	Delaware	Chemicals	516,667	DuPont
10	35203	34	-87	3,301	POINT (-86.80999 33.51847)	Alabama	Commercial Banks	479,592	Regions Financial
11	50021	42	-94	29,718	POINT (-93.56838 41.7207)	Iowa	Specialty Retailers: Other	450,151	Casey's General Stores
12	02903	42	-71	12,309	POINT (-71.41003 41.81902)	Rhode Island	Aerospace & Defense	402,941	Textron